Waternarking Digital Image and Video Data

20 IEEE SIGNAL PROCESSING MAGAZINE SEPTEMBER 2000

A State-of-the-Art Overview

In the past decade there has been an explosion in theuse and distribution of digital multimedia data. PCswith Internet connections have taken homes bystorm and have made the distribution of multimediadata and applications much easier andfaster. Electronic commerce applica-tions and on-line services are rapidlybeing developed. Even the analog au-dio and video equipment in the homeis in the process of being replaced bytheir digital successors. As a result, wecan see the digital mass recording devices for multimediadata enter the consumer market of today.

The Need for WatermarkingAlthough digital data has many advantages over analogdata, service providers are reluctant to offer services in dig-ital form because they fear unrestricted duplication anddissemination of copyrighted material. Because of possi-ble copyright issues, the intellectual property of digitallyrecorded material must be protected [90]. The lack ofsuch adequate protection systems for copyrighted contentwas the reason for the delayed introduction of the digitalversatile disk (DVD) [100]. Several media companies ini-tially refused to provide DVD material until the copy pro-tection problem had been addressed [89], [81].Representatives of the consumer electronics industry andthe motion picture industry have agreed to seek legislationconcerning digital video recording devices. Recommen-dations describing ways that would protect both intellec-tual property and consumers rights have been submittedto the U.S. Congress [81] and resulted in the Digital Mil-lennium Copyright Act [25], which was signed by Presi-dent Clinton on October 28, 1998. The European Unionis also preparing such intellectual property rights protec-tion for digital multimedia products including CDs orDVDs [28].

To provide copy protection and copyright protectionfor digital audio and video data, two complementary tech-niques are being developed: encryption and watermark-ing [23]. Encryption techniques can be used to protect

digital data during the transmissionfrom the sender to the receiver [63].After the receiver has received and de-crypted the data, however, the data isidentical to the original data and nolonger protected. Watermarkingtechniques can compliment encryp-

tion by embedding a secret imperceptible signal, a water-mark, directly into the original data in such a way that italways remains present. Such a watermark, for instance,can be used for the following purposes: Copyright Protection: For the protection of intellectualproperty, the data owner can embed a watermark repre-senting copyright information in his data. This watermarkcan prove his ownership in court when someone has in-fringed on his copyrights. Fingerprinting: To trace the source of illegal copies, theowner can use a fingerprinting technique. In this case, theowner can embed different watermarks in the copies of thedata that are supplied to different customers. Finger-printing can be compared to embedding a serial numberthat is related to the customers identity in the data. It en-ables the intellectual property owner to identify customerswho have broken their license agreement by supplying thedata to third parties. Copy Protection: The information stored in a watermarkcan directly control digital recording devices for copy pro-tection purposes [62]. In this case, the watermark repre-sents a copy-prohibit bit and watermark detectors in therecorder determine whether the data offered to the re-corder may be stored or not. Broadcast Monitoring: By embedding watermarks incommercial advertisements, an automated monitoringsystem can verify whether advertisements are broadcasted

1053-5888/00/$10.002000IEEE

Gerhard C. Langelaar,Iwan Setyawan, and

Reginald L. Lagendijk

as contracted [3]. Not only commercials but also valuableTV products can be protected by broadcast monitoring[53]. News items can have a value of over US$100,000per hour, which make them very vulnerable to intellectualproperty rights violation. A broadcast surveillance systemcan check all broadcast channels and charge the TV sta-tions according to their findings. Data Authentication: Fragile watermarks [108] can beused to check the authenticity of the data. A fragile wa-termark indicates whether the data has been altered andsupplies localization information as to where the datawas altered.

Watermarking techniques are not only used for pro-tection purposes. Other applications include: Indexing: Indexing of video mail, where comments canbe embedded in the video content; indexing of moviesand news items, where markers and comments can be in-serted that can be used by search engines. Medical Safety: Embedding the date and the patientsname in medical images could be a useful safety mea-sure [3]. Data Hiding: Watermarking techniques can be usedfor the transmission of secret private messages. Since vari-ous governments restrict the use of encryption services,people may hide their messages in other data.

Some authors, for example in [11], refer to water-marking technique only when the application embeds afew bits (as few as one bit) of data for copyright no-tice/protection applications. Other applications are con-sidered to fall into the category of data embedding. Weprefer to use the term watermarking, however, for allthese applications in this article. In our opinion,watermarking has nowadays been used for applicationsbeyond the limits of copy protection/authentication, anexample of which is Digimarcs Smart Images [1].

Watermarking RequirementsEach watermarking application has its own specific re-quirements. Therefore, there is no set of requirements tobe met by all watermarking techniques. Nevertheless,some general directions can be given for most of the ap-plications mentioned above: Perceptual Transparency: In most applications thewatermarking algorithm must embed the watermarksuch that this does not affect the quality of the underlyinghost data. A watermark-embedding procedure is trulyimperceptible if humans cannot distinguish the originaldata from the data with the inserted watermark [97].Even the smallest modification in the host data may be-come apparent, however, when the original data is com-pared directly with the watermarked data. Since users ofwatermarked data normally do not have access to theoriginal data, they cannot perform this comparison.Therefore, it may be sufficient that the modifications inthe watermarked data go unnoticed as long as the data arenot compared with the original data [103].

Payload of the Watermark: The amount of informationthat can be stored in a watermark depends on the applica-tion. For copy protection purposes, a payload of one bit isusually sufficient.

According to a recent proposal for audio watermark-ing technology from the International Federation forthe Phonographic Industry (IFPI), the minimum pay-load for an audio watermark should be 20 bits per sec-ond, independently of the signal level and music type[46]. According to [75], however, this minimum isvery ambitious and should be lowered to only a few bitsper second.

For the protection of intellectual property rights, itseems reasonable to assume that one wants to embed anamount of information similar to that used for ISBN, In-ternational Standard Book Numbering (roughly 10 dig-its) or better ISRC, International Standard RecordingCode (roughly 12 alphanumeric letters). On top of this,one should also add the year of copyright, the permissionsgranted on the work, and the rating for it [59]. Thismeans that about 60 bits [31] or 70 bits [59] of informa-tion should be embedded in the host data, the image, thevideo frame, or the audio fragment.

Another important concept regarding watermark pay-load for digital audio and video is watermark granularity.Watermark granularity represents how much data isneeded to embed one unit of watermark information.Using the example above, one unit of watermark infor-mation consists of 60 or 70 bits. This could be embeddedin a single frame of video or spread, for instance, over 100frames of video (or similarly for audio, the watermarkcould be embedded in a 1-s fragment or spread for in-stance over 5 s of audio data). Spreading the watermark inthis way may not be desirable because when someonetakes just 80 frames from the watermarked video, the wa-termark information is no longer retrievable. For digitalvideos, 1 s of video is considered to be the smallest copy-righted entity. Therefore, the watermark information hasto be embedded in a less than 1 s fragment of the videostream (approximately 25 frames). Again using the exam-ple above, the watermark bit rate should then be morethan 70 bits/s. Robustness: A fragile watermark that has to prove theauthenticity of the host data does not have to be robustagainst processing techniques or intentional alterations ofthe host data, since failure to detect the watermark provesthat the host data has been modified and is no longer au-

SEPTEMBER 2000 IEEE SIGNAL PROCESSING MAGAZINE 21

A watermark-embeddingprocedure is imperceptible ifhumans cannot distinguish theoriginal data from the data withthe inserted watermark.

thentic. If a watermark is used for another application,however, it is desirable that the watermark always re-mains in the host data, even if the quality of the host datais degraded, intentionally or unintentionally. Examples ofunintentional degradations are applications involvingstorage or transmission of data, where lossy compressiontechniques are applied to the data to reduce bit rates andincrease efficiency. Other unintentional quality-degrad-ing processing techniques include filtering, re-sampling,digital-analog (D/A) and analog-digital (A/D) conver-sion. On the other hand, a watermark can also be sub-jected to processing solely intended to remove thewatermark [23]. In addition, when many copies of thesame content exist with different watermarks, as wouldbe the case for fingerprinting, watermark removal is pos-sible because of collusion between several owners of cop-ies. In general, there should be no way in which thewatermark can be removed or altered without sufficientdegradation of the perceptual quality of the host data soas to render it unusable. Security: The security of watermarking techniques canbe interpreted in the same way as the security of encryp-tion techniques. Kerckhoffs assumption states that oneshould assume that the method used to encrypt the data isknown to an unauthorized party and that the securitymust lie in the choice of a key [69]. Hence a watermark-ing technique is truly secure if knowing the exact algo-rithms for embedding and extracting the watermark doesnot help an unauthorized party to detect the presence ofthe watermark or remove it [97].

Oblivious versus Nonoblivious Watermarking: In someapplications, like copyright protection and data monitor-ing, watermark extraction algorithms can use the originalunwatermarked data to find the watermark. This is callednonoblivious watermarking [59]. In most other applica-tions, e.g., copy protection and indexing, the water-mark-extraction algorithms do not have access to theoriginal unwatermarked data. This renders the water-mark extraction more difficult. Watermarking algorithmsof this kind are referred to as public, blind, or obliviouswatermarking algorithms.

The requirements listed above are all related to eachother. For instance, a very robust watermark can be ob-tained by making many large modifications to the hostdata for each bit of the watermark. Large modifications inthe host data will be noticeable, however, and many modi-fications per watermark bit will limit the maximumamount of watermark bits that can be stored in a data ob-ject. Hence, a tradeoff should be considered between thedifferent requirements so that an optimal watermark foreach application can be developed. The mutual dependen-cies between the basic requirements are shown in Fig. 1.

The relation between the basic requirements for awell-designed secure watermark is represented in Fig. 2.The perceptual impact axis represents the quality degra-dation of the data due to watermarking. The higher theperceptual impact, the worse the quality degradation.The payload axis represents the amount of data that couldbe embedded in the data. The robustness axis representsthe ability of the watermarking system to resist attacks.The security of a watermark influences the robustnessenormously. If a watermark is not secure, it cannot bevery robust.

Scope of the ArticleTo embed watermark information in host data, water-mark embedding techniques apply minor modificationsto the host data in a perceptually invisible manner, wherethe modifications are related to the watermark informa-tion. The watermark information can be retrieved after-wards from the watermarked data by detecting thepresence of these modifications.

A wide range of modifications in any domain can beused for watermarking techniques. Prior to embeddingor extracting a watermark, the host data can be converted,for instance, to the spatial, the Fourier, the wavelet, thediscrete cosine transform or even the fractal domain,where the properties of the specific transform domainscan be exploited. In these domains modifications can bemade, like least significant bit (LSB) modification, noiseaddition, coefficient re-ordering, coefficient removal,warping or morphing data parts, and block similaritiesenforcing. Further, the impact of the modifications canbe minimized with the aid of human visual models,whereas modifications can be adapted to the anticipated


Perceptual Transparency

Payload Robustness Security

Oblivious versus Nonoblivious

1. Mutual dependencies between the basic requirements.

Per

cept

ual I

mpa

ct

PayloadRo

bustn

ess

Oblivious

Nonoblivious

2. Illustration of the relation between the basic requirementsfor a secure watermark.

post-processing techniques or to the compression formatof the host data.

Since the most commonly used techniques use addi-tive noise for watermark embedding and correlation tech-niques for watermark detection, we discuss the obliviouscorrelation-based techniques extensively in this article,together with all its possible variations. Other oblivioustechniques are explained as well. The cryptographic secu-rity of the methods described here lies in the key that isused to generate a pseudorandom watermark pattern orto pseudorandomly select image regions or coefficients toembed the watermark. In general, the robustness of thewatermark against processing techniques depends on theembedding depth and the amount of information bits ofthe watermark.

The article is organized as follows. First we will discussdigital watermarking techniques based on correlation inthe next two sections. And then we will discuss digitalwatermarking techniques that are not based on correla-tion. The last section presents some conclusion of the arti-cle including a brief discussion of recent developments inthe digital watermarking area.

Correlation-BasedWatermarking TechniquesBasic Technique in the Spatial DomainThe most straightforward way to add a watermark to animage in the spatial domain is to add a pseudorandomnoise pattern to the luminance values of its pixels. Manymethods are based on this principle [91], [10], [76],[18], [36], [35], [77], [93], [105], [61], [106], [113],[32], [107], [108], [53]. In general, the pseudorandomnoise pattern consists of the integers {1,0,1}, however,also floating-point numbers can also be used. The patternis generated based on a key using, for instance, seeds, lin-ear shift registers or randomly shuffled binary images.The only constraints are that the energy in the pattern is

more or less uniformly distributed and that the pattern isnot correlated with the host image content. To create thewatermarked image I x yW ( , ) the pseudorandom patternW x y( , ) is multiplied by a small gain factor k and added tothe host image I x y( , ), as illustrated in Fig. 3

I x y I x y k W x yW ( , ) ( , ) ( , )= + . (1)

To detect a watermark in a possibly watermarked im-age I x yW ( , ) we calculate the correlation between theimage I x yW ( , ) and the pseudorandom noise patternW x y( , ). In general,W x y( , ) is normalized to a zero meanbefore correlation. Pseudorandom patterns generated us-ing different keys have very low correlation with eachother. Therefore, during the detection process the corre-lation value will be very high for a pseudorandom patterngenerated with the correct key and would be very low


Multiply by GainFactor k

I x,y( ) I x,yW( )

k

W x,y( ): Pseudorandom Pattern {1,0,1}

3. Watermark embedding procedure.

Cor

rela

tion

Val

ue

300

250

200

150

100

50

0

500 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Random Number Seed

4. Correlation values for a pseudorandom pattern generatedwith seed=10 correlated with pseudorandom patterns gener-ated with other seeds.

To add a watermark to animage in the spatial domain,add a pseudorandom noisepattern to the luminance valuesof its pixels.

False Positive

False Negative

0 T 2k [ ] [ ] I Iw w+

5. Watermark detection procedure.

otherwise. This is shown in Fig. 4. Here we have water-marked the Lena image by adding a pseudorandom pat-tern generated using with seed = 10 to the image. Figure4 shows the correlation values of some pseudorandompatterns generated using seeds varying between 0 and 15to the watermarked image. It can be seen that the correla-tion when the correct seed (10) is used is very high,while the correlation when the wrong seeds are used arevery low.

During the detection process, it is common to set athreshold T to decide whether the watermark is detectedor not. If the correlation exceeds a certain threshold T, thewatermark detector determines that image I x yW ( , )con-tains watermark W x y( , )

R T W x yT W x y

I x y W x yW>

< ( , ) ( , ) ( , )

( , )detected

No detected.(2)

IfW x y( , )only consists of the integers{ , }1 1 and if thenumber of 1s equals the number of 1s, we can estimatethe correlation as

RN

I x y W x y

NI W

I x y W x y W ii

N

W ii

W i

i

=

+

=

=

( , ) ( , ) ( , ) ( , )1

1

1

[ ] [ ]{ }=

=

+

+

=

1

2

1

21

12

N

W ii

N

W W

NI W

I x y I x y

i

/ /

( , ) ( , ) . (3)

Here N is the number of pixels in the image I W , and+, indicates the set of pixels where the correspondingnoise pattern is positive or negative, and [ ( , )]I x yW

+

represents the average value of set pixels in I x yW+ ( , ).

From (3) it follows that the watermark detection prob-lem corresponds to testing the hypothesis whether tworandomly selected sets of pixels in a watermarked imagehave the same mean.

During the detection process, the watermark detectorcan make two types of errors. In the first place, it can de-

tect the existence of a watermark, although there is none.This is called a false positive. In the second place, the detec-tor can reject the existence of the watermark, even thoughthere is one. This is called a false negative. The probabilityfunction for the detection process is presented in Fig. 5.

In [52] the probabilities of these two types of errorsare derived based on a first-order autoregressive imagemodel:

P T N

PT N

fpW I

fnW

W I

=

=

12 2

12

2

erfc and

erfc

( )

2

12

2 2

=

where erfc( ) ./x e dttx (4)

Here, Pfp represents the probability of false positive,Pfn represents the probability of false negative, W

2 repre-sents the variance of the watermark pixels and I

2 denotesthe variance of the image pixels. If the watermark patternW x y( , )only consists of the integers { , }1 1 and the num-ber of -1s equals the number of 1s, the variance of the wa-termark W

2 equals k2 . The errors Pfp and Pfn can beminimized by increasing the gain factor k. Using larger


I0

b0

I5

b5

I10

b10

I24

b24

I1

b1

I6

b6

I2

b2

I7

b7

I3

b3

I8

b8

I4

b4

I9

b9

k

I x,y( ) I x,yW( )

Random Pattern {1,0,1}

b=0

b=1

: 1

: 1

WM: b b b0 1 1L

6. Watermark bit string embedding procedure.

k P=2, =32 32

Without Prefilter Fedge

With Prefilter Fedge

% B

it E

rror

s

60

50

40

30

20

10

00 10 20 30 40 50 60 70 80 90 100

Qjpeg

7. Watermark detection with and without prefiltering.

% B

it E

rror

s

60

50

40

30

20

10

00 10 20 30 40 50 60 70 80 90 100

Qjpeg

k=2k=3

k=1

P=32 32, Prefilter Applied before DetectionFedge

8. Influence of the gain factor k on the robustness of a water-mark.

values for the gain factor, however, decreases the visualquality of the watermarked image.

Since the image content can interfere with the water-mark, especially in the low-frequency components, thereliability of the detector can be improved by applyingmatched filtering before correlation [26], [91], [35].This decreases the contribution of the original image tothe correlation. For instance, a simple edge-enhancing fi-nite impulse response (FIR) filter Fedge can be used,where Fedge is given by the following convolution kernel:

Fedge =

1 1 11 10 11 1 1

2/ .

(5)

The experimental results presented in the next sec-tion show that applying thisfilter before correlation re-duces the error probabilitysignificantly, even when thevisual quality of the water-marked image was affected se-riously before correlation[35], [61]. In [67], the au-thors proposed another wayto improve the robustness ofthe watermark. The robust-ness improvement is achievedby performing a spectrumequalization prior to water-mark embedding.

Extensions to EmbedMultiple Bitsor Logos in One ImageFrom the watermark detectorspoint of view, an image I can beregarded as Gaussian noise,which distorts the watermarkinformation W. Further, the

watermarked image I W can be seen as the output of acommunication channel subject to Gaussian noise overwhich the watermark information is transmitted. In thiscase, reliable transmission of the watermark is theoreti-cally possible if its information rate does not exceed thechannel capacity, which is given by [92]

C WbW

I

= +

log 2

2

21

bit/pixel.

(6)

Here, C is given in units of watermark informationbits per image pixel and the available bandwidth Wb isequal to one cycle per pixel. For practical systems, how-ever, a tighter empirically lower bound can be determined[93]

C WbW

I

= +

log 2

2

21

bit/pixel.

(7)

Here, is a small headroom factor, which is largerthan one and typically around three. Since the sig-nal-to-noise ratio W I

2 2/ is significantly smaller thanone, (7) can be approximated by

C W

I

1

2

2

2ln

bit/pixel.

(8)

According to this equation, it should be possible tostore much more information in an image than just 1 bitusing the basic technique described in the previous sec-tion. For instance, a watermark consisting of the integers


RP0RPRPRPRPRPRP

1

2

3

4

5

6

+RP0+RP

RPRP

+RPRP

+RP

1

2

3

4

5

6

b0bbbbbb

1

2

3

4

5

6W

10. Example of a CDMA watermark generation for 7 bits b b b0 1 7 .

WI

IW

E [ ( E[ ] ) ( E[ ] ) ]RP RP I0 0 " W IWE [ ( E[ ] ) ( E[ ] ) ]RP RP I1 1 " W IWE [ ( E[ ] ) ( E[ ] ) ]RP RP I2 2 " W IWE [ ( E[ ] ) ( E[ ] ) ]RP RP I3 3 " W IWE [ ( E[ ] ) ( E[ ] ) ]RP RP I4 4 " W IWE [ ( E[ ] ) ( E[ ] ) ]RP RP I5 5 " W IWE [ ( E[ ] ) ( E[ ] ) ]RP RP I6 6 " W IW

b0 = 0

b1 = 0

b2 = 1

b3 = 1

b4 = 0

b5 = 1

b6 = 0

11. Example of CDMA watermark extraction, compare to Fig. 10.

% B

it E

rror

s60

50

40

30

20

10

00 10 20 30 40 50 60 70 80 90 100

Qjpeg

P=8 8

P=16 16

P=32 32P=64 64

P=128 128

k=2, Prefilter Applied before DetectionFedge

9. Influence of the number of pixels per watermark bit P on therobustness of a watermark.

{ , }k k added to the 512 512 Lena image (Fig. 3) cancarry approximately 50, 200, or 500 bits of informationfor k =1 2, , or 3 respectively and for = 3.

There are several ways to increase the payload of thebasic watermarking technique. The simplest way to em-bed a string of l watermark bits b b bl0 1 1 in an image is todivide the image I into l subimages I I I l0 1 1 and to adda watermark to each subimage, where each watermarkrepresents one bit of the string [93], [35], [61]. This pro-cedure is depicted in Fig. 6.

Using (8) we can calculate the number of pixels P re-quired per subimage for reliable de-tection of a single bit in a subimage

P IW

2

2

2lnpixels.

(9)

The watermark bits can be repre-sented in several ways. A pseudoran-dom pattern can be added if thewatermark bit equals one, and thesubimage can be left unaffected if thewatermark bit equals zero. In thiscase, the detector calculates the corre-lation between the subimage and thepseudorandom pattern and assignsthe value 1 to the watermark bit if thecorrelation exceeds a certain thresh-old T; otherwise the watermark bit isassumed to be zero.

The use of a threshold can be cir-cumvented by adding two differentpseudorandom patterns RP0 and RP1for watermark bit 0 and 1. The detec-tor now calculates the correlation be-tween the subimage and the twopatterns. The bit value correspondingwith the pattern that gives the highestcorrelation is assigned to the water-mark bit. In [93] the two patterns arechosen in such a way that they onlydiffer in sign, RP RP0 1= . In this

case, the detector only has to calculate the correlation be-tween the subimage and one of the patterns; the sign of thecorrelation determines the watermark bit value.

To investigate the effect on the robustness of the wa-termark of the prefilter in the detector, the gain factor k,and the number of pixels P per watermark bit, we performthe following experiments. We first add a watermark toan image with the method of [93]. Next, we compress thewatermarked image with the JPEG algorithm [73],where the quality factor Q jpeg of the compression algo-rithm is made variable. Finally, the watermark is extractedfrom the decompressed image and compared bit by bitwith the originally embedded watermark bits. From thisexperiment, we find the percentages of watermark bit er-rors due to JPEG compression as a function of the JPEGquality factor.

The first experiment shows the effect of applying theprefilter given by (5) before detection of a watermark em-bedded with a gain factor k =2, and P = 32 32 pixels perwatermark bit. In Fig. 7 the percentages bit errors causedby JPEG compression are plotted for a detector that usesthis prefilter and for a plain detector. It can clearly be seenthat prefiltering significantly increases the robustness ofthe watermark.

The second experiment shows the effect of increasingthe gain factor k for a watermark embedded with


Extracted Logo from ImageCompressed with JPEG =50Q



Original EmbeddedWatermark Logo

12. Extracted watermark logos from a JPEG distorted image.

(a) (b)

(c) (d)

13. Fourier amplitude watermark. (a) Original image, (b) watermarked image, (c) dif-ference W x y I Iw( , ) = scaled for visibility, and (d) heavily marked image.

P = 32 32 pixels per watermark bit and detected using aprefilter. From Fig. 8 it follows that the robustness of awatermark can be improved significantly by increasingthe gain factor.

The third experiment shows the influence of the num-ber of pixels P per watermark bit on the robustness of awatermark embedded with a gain factor k =2 and de-tected using a prefilter. From Fig. 9 it follows that de-creasing the payload of the watermark by increasing Pimproves the robustness significantly.

Another way to increase the payload of the basicwatermarking technique is the use of direct sequencecode division multiple access (DS-CDMA) spread spec-trum communications [87], [88]. Here, for each bit bjout of the watermark bit string b b bl0 1 1 a differentstochastically independent pseudorandom pattern RPi isgenerated that has the same size as the image. This patternis dependent on the bit value bj . Here we use the pattern+RPi if bj represents a 0 and RPi if bj represents a 1. Thesummation of all l random patterns RPi forms the wa-termark. Prior to adding the watermark to an image, wecan scale the watermark by a gain factor or limit it to a cer-tain small range. An example of the one-dimensional wa-termark generation is presented in Fig. 10. This exampleuses seven different pseudorandompatterns to embed the seven water-mark bits 0011010.

Each bit bj out of the watermarkbit string b b bl0 1 1 can be extractedby calculating the correlation be-tween the normalized image I W andthe corresponding pseudorandompattern RPi . If the correlation is pos-itive, the value 0 is assigned to thewatermark bit, otherwise the water-mark bit is assumed to be one. Fig-ure 11 shows as an example theextraction of the embedded water-mark bits in Fig. 10.

The methods to extend the water-mark payload described above,namely using individual image tilesfor each watermark bit and usingCDMA, have their advantages anddisadvantages. If each watermark bithas its own image tile, there is no in-terference between the bits and only asmall number of multiplications arerequired to calculate the correlations.If the image is cropped, however, thewatermark bits located at the borderare lost. If CDMA techniques areused, the probability that all bits canbe recovered after cropping the imageis high. The watermark bits may inter-fere with each other, however, andmany multiplications are required to

calculate the correlations, since each bit is completelyspread over the image.

The watermark bits embedded using the methodsmentioned above can represent anything: copyright mes-sages, serial numbers, plain text, control signals, etc. Thecontent represented by these bits can be compressed, en-crypted, and protected by error correcting codes. In somecases it may be more useful to embed a small logo insteadof a bit string as a watermark. If the watermarked image isdistorted, the watermark logo will also be affected. Butnow the sophisticated pattern-recognition capabilities ofthe human visual system (HVS) can be exploited to de-tect the logo [15], [45], [102]. For instance, we can em-bed a binary watermark logo with 128 32 pixels in animage with 512 512 pixels using the techniques de-scribed in this section. Each logo pixel is embedded in animage tile of 8 8 pixels by adding the pseudorandompattern +RP or RP to the image tile for a black or whitelogo pixel respectively. As an example in Fig. 11 the re-sults are shown of the logos extracted after the water-marked image has been degraded with the lossy JPEG[73] compression algorithm using several quality factors.From Fig. 12 it can be seen that, although it is heavily cor-rupted, the logo can still be recognized.


(a)

(c)

(b)

fv

fh

(d)

14. An 8 8 DCT middle band image content independent watermark. (a) Water-marked image, (b) a heavily watermarked image, (c) differenceW x y I x y I x yw( , ) ( , ) ( , )= , and (d) Fourier spectrum W u( , ) .

Techniques for Transform DomainsThe techniques described in the previous section can alsobe applied on transformed image data. Each transformdomain has it own advantages and disadvantages. In [85]the phase of the discrete Fourier transform (DFT) is usedto embed a watermark, because the phase is more impor-tant than the amplitude of the DFT values for the intelli-gibility of an image. Putting a watermark in the mostimportant components of an image improves the robust-ness of the watermark, since tampering with these impor-tant image components to remove the watermark willseverely degrade the quality of the image. The second rea-son to use the phase of the DFT values is that it is wellknown from communication theory that phase modula-tion often possesses superior noise immunity in compari-son with amplitude modulation [85].

Many watermarking techniques use DFT amplitudemodulation because of its translation or shift invariantproperty [40], [41], [74], [83], [86]-[88]. Because cy-clic translation of the image in the spatial domain doesnot affect the DFT amplitude, the watermark embeddedin this domain will be translation invariant. In case aCDMA watermark is used, it is even slightly resistant tocropping. Furthermore, the watermark can be embed-ded directly in the most important middle band fre-quencies, since modulation of thelowest frequency coefficients resultsin visible artifacts while the highestfrequency coefficients are very vul-nerable to noise, filtering, and lossycompression. Finally the watermarkcan easily be made image content de-pendent by modulating the DFTamplitude coefficients |I(u,v)| inthe following way [20]:

| | | | ( )I u v I u v k W u vW ( , ) ( , ) ( , )= + 1 .(10)

H e r e , W u v( , ) r e p r e s e n t s aCDMA watermark, a two-dimen-sional (2-D) pseudorandom pat-tern, and k denotes the gain factor.Now, the modification of a DFTcoefficient is not fixed but propor-tional to the amplitude of the DFTcoefficient. Small DFT coeffi-cients are hardly affected, whereaslarger DFT coefficients are af-fected more severely. This com-plies with Webers law [50]. TheHVS does not perceive equalchanges in images equally, but vi-sual sensitivity is nearly constantwith respect to relative changes inan image. If I is a just noticeabledifference, then I I/ = constant.Rewriting (10) gives

| | | || | | |

I u v I u v

I u vI u v

I u vk W u vW

( , ) ( , )

( , )( , )( , )

( , )

= =

constant.(11)

Since the watermark here is mainly embedded in thelarger DFT coefficients, i.e., the perceptually most signif-icant components of the image, the robustness of the wa-termark improves.

Note that the symmetry of the Fourier coefficientsmust be preserved to ensure that the image data is stillreal valued after the inverse transform to the spatial do-main. If the coefficient | |I u v( , ) in an image with N Mpixels is modified according to (10), its counterpart


y

x

Image I

FM

8 8 DCTv

u

15. Definition of the middle band frequencies in a DCT block.

(a) (b)

fh

fv

(c) (d)

16. An 8 8 block DCT middle band image content dependent watermark. (a) Water-marked image, (b) a heavily watermarked image, (c) differenceW x y I x y I x yw( , ) ( , ) ( , )= , and (d) Fourier spectrum W u( , ) .

| ( , )|I N u M v must be modified in the same way. InFig. 13(b) an example is given of an image in which a wa-termark is embedded using all DFT amplitude coeffi-cients according to (10) and using a relatively small gainfactor k. Figure 13(c) presents the strongly amplified dif-ference between the original image and the watermarkedimage. Figure 13(d) shows an image watermarked usinga large value of the gain factor k.

Another commonly used domain for embedding awatermark is the discrete cosine transform (DCT) do-main [12], [20]-[22], [45], [78], [79], [99], [84],[110]. Using the DCT an image can easily be split up inpseudo frequency bands, so that the watermark canconveniently be embedded in the most important mid-dle band frequencies. Furthermore, the sensitivity ofthe HVS to the DCT basis images has been extensivelystudied, which resulted in the recommended JPEGquantization table [73]. These results can be used forpredicting and minimizing the vi-sual impact of the distortion causedby the watermark. Finally, theblock-based DCT is widely used forimage and video compression. Byembedding a watermark in the samedomain as the compression schemeused to process the image (in thiscase in the DCT domain) we can an-ticipate lossy compression becausewe are able to anticipate whichDCT coefficients will be discardedby the compression scheme. Fur-thermore, we can exploit the DCTdecomposition to make real-timewatermark applications.

In Fig. 14(a) an example is given ofan image in which a 2-D CDMA wa-

termark W is embedded in the 8 8 block DCT middleband frequencies. The 8 8 DCT coefficients F u v( , ) aremodulated according to the following:

I u vI u v k W u v u v FI u v uW

x y x y M

x yx y,

( , )( , ) ( , ), ,( , ),

, ,

,

=+

,

, , , ,... .

v F

x yM

=1 8 16 (12)

Here FM denotes the middle band frequencies, k thegain factor, ( , )x y the spatial location of an 8 8 pixelblock in image I, and( , )u v the DCT coefficient in the cor-responding 8 8 DCT block (Fig. 15).

In Fig. 14(c) the strongly amplified difference be-tween the original image and the watermarked image ispresented. Figure 14(d) shows the Fourier spectrum ofthe watermark. Here, it can clearly be seen that water-mark only affects the middle band frequencies (white re-gions) while leaving lower and high frequencycomponents relatively unaffected (dark regions).

The watermark can be made image dependent bychanging the modulation function to [c.f. (10)]

I u vI u v k W u v u v FI uW

x y x y M

x yx y,

( , )( , ) ( ( , )), ,( ,

, ,

,

= + 1

v u v F

x yM), ,

, , , ,... .

=1 8 16 (13)

If this modulation function is applied, the results fromFig. 13 change into the results shown in Fig. 16. FromFig. 16(b) and (c) it appears that most distortion intro-duced by the watermark is located around the edges andin the textured areas.

Further improvements for DCT-domain correla-tion-based watermarking systems performance couldbe achieved by using watermark detectors based ongeneralized Gaussian model, instead of the widely usedpure Gaussian assumption [42]. By performing a theo-retical analysis for DCT-domain watermarking meth-ods for images, the authors in [42] provide analytical


LL2 HL2

LH2 HH2

HL1

L 1H HH1

17. DWT two-level decomposition of an image.

(a) (b)

18. DWT image content independent watermark. (a) A heavily watermarked image and(b) difference W x y I x y I x yw( , ) ( , ) ( , )= .

expressions which can be used to measure beforehandthe performance that can be expected for a certain im-age and to analyze the influence of the image character-istics and system parameters (e.g., watermark length)on the final performance. Furthermore, the result ofthis analysis can help determining the proper detectionthreshold T to obtain a certain false positive rate. Theauthors in [42] claim that by abandoning the pureGaussian noise assumption, some substantial perfor-mance improvements could be obtained.

If watermarking techniques can exploit the character-istics of the HVS, it is possible to hide watermarks withmore energy in an image, which makes watermarks morerobust. From this point of view the discrete wavelet trans-form (DWT) is a very attractive transform, because it canbe used as a computationally efficient version of the fre-quency models for the HVS [7]. For instance, it appearsthat the human eye is less sensitive to noise in high resolu-tion DWT bands and in the DWT bands having an orien-tation of 45 (i.e., HH bands). Furthermore, DWT imageand video coding, such as embedded zero-tree wavelet(EZW) coding, will be included in the upcoming imageand video compression standards, such as JPEG2000[112]. By embedding a watermark in the same domain(DWT domain) we can anticipate lossy EZW compres-sion because we can anticipate which DWT bands is go-ing to be affected by the compression scheme.Furthermore, we can exploit the DWT decomposition tomake real-time watermark applications. Many ap-proaches apply the basic techniques described at the be-ginning of this section to the high resolution DWTbands, LH1 , HH1 , and HL1 (Fig. 17) [7], [12], [56],[84], [112].

In Fig. 18(a) an example is given of an image in whicha 2-D CDMA watermark W is embedded in the LH1 ,HH1 , and HL1 DWT bands using a large gain factor k.The DWT coefficients in each of the three DWT bandsare modulated as follows:

I u v I u v k W u vW ( , ) ( , ) ( , )= + . (14)

Figure 18(b) shows the stronglyamplified difference between theoriginal image and the watermarkedimage.

The DWT watermark can be madeimage dependent by modulating theDWT coefficients in each of the threeDWT bands as follows:

I u v I u v k W u vW ( , ) ( , ) ( ( , )).= + 1 (15)In Fig. 19(a) an example is given

of an image in which the sameCDMA watermark W is embeddedin the LH1 , HH1 , and HL1 DWTbands using (15) with a large gainfactor k. Figure 19(b) shows thestrongly amplified difference be-

tween the original image and the watermarked image.

Watermark Energy Adaptation Based on HVSThe robustness of a watermark can be improved by in-creasing the energy of the watermark. Increasing the en-ergy, however, degrades the image quality. Byexploiting the properties of the HVS, the energy can beincreased locally in places where the human eye will notnotice it. As a result, by exploiting the HVS, one can em-bed perceptually invisible watermarks that have higherenergy than if this energy were to be distributed evenlyover the image.

If a visual signal is to be perceived, it must have a mini-mum amount of contrast, which depends on its mean lu-minance and frequency. Furthermore, a signal of a givenfrequency can mask a disturbing signal of a similar fre-quency [104], [6]. This masking effect is already used inthe image-dependent DCT watermarking method de-scribed in the previous section, where the DCT-coeffi-cients are modulated by means of (13). Here, to eachsinusoid present in the image (masking signal), anothersinusoid (watermark) is added, having an amplitude pro-portional to the masking signal. If the gain factor k isproperly set, frequency masking occurs.

The HVS is less sensitive to changes in regions of highluminance. This fact can be exploited by making the wa-termark gain factor luminance dependent [58]. Further-more, since the human eye is least sensitive to the bluechannel, a perceptually invisible watermark embedded inthe blue channel can contain more energy than a percep-tually invisible watermark embedded in the luminancechannel of a color image [58].

Around edges and in textured areas of an image, theHVS is less sensitive to distortions than in smooth areas.This effect is called spatial masking and can also be ex-ploited for watermarking by increasing the watermarkenergy locally in these masked image areas [68]. The basic


(a) (b)

19. DWT image content dependent watermark. (a) A heavily watermarked image and(b) difference W x y I x y I x yw( , ) ( , ) ( , )= .


spatial watermarking techniques described in the first twosubsections of this section can be extended with spatialmasking compensation, for instance, by using the follow-ing modulation function:

I x y I x y Msk x y k W x yW ( , ) ( , ) ( , ) ( , )= + . (16)

Here W x y( , ) represents the 2-D pseudorandom pat-tern of the watermark, k denotes the fixed gain factor, andMsk x y( , ) represents a masking image. The values of themasking image range from 0 to k max and give a measureof insensitivity to distortion for each corresponding pointin the original image I x y( , ). In [53] the masking imageMsk is generated by filtering the original image with aLaplacian high-pass filter and by taking the absolute val-ues of the resulting filtered image.

In Fig. 20(a) a mask is shown for the Lena image [Fig.13(a)] which is generated by a simple Prewitt edge detec-tor [71]. Figure 20(b) shows the strongly amplified wa-termark modulated with this mask.

In [70] the squared sum of the 8 8 DCT AC-coeffi-cients is used to generate a maskingimage. Figure 21(a) shows a maskgenerated using this DCT-ac energyfor the Lena image. Figure 21(b)presents the strongly amplified wa-termark modulated with this mask.

Experiments have shown that aperceptually invisible watermarkmodulated with a gain factor locallyadapted to such a mask can containtwice as much energy as a perceptu-ally invisible watermark modulatedwith a fixed gain factor.

To investigate the effect of thisenergy doubling on the robustnessof the watermark, we perform thefollowing experiment. We add a wa-termark W x yfixed ( , ) to the Lena im-age with the tiled spread spectrumwatermarking method described in[93] using a fixed gain factor k =2.Increasing this fixed gain factorcauses visible artifacts in the result-ing watermarked image. Next, weadd a watermark W x yvar ( , ) to an-other Lena image with the samemethod, but now we use a variablegain factor locally adapted to themasking image presented in Fig.19(a). Although the watermarkW x yvar ( , ) contains about twice asmuch energy as W x yfixed ( , ) the wa-termark is not noticeable in the re-sulting watermarked image. Thenwe compress both watermarked im-

ages with the JPEG algorithm [73], where the qualityfactor Q jpeg of the compression algorithm is made vari-able. Finally, the watermarks are extracted from the de-compressed image and compared bit by bit with theoriginally embedded watermark bits. From this experi-ment, we find the percentages of watermark bit errorsdue to JPEG compression as a function of the JPEGquality factor. In Fig. 22 the error curves are plotted forboth watermarks W x yfixed ( , ) and W x yvar ( , ). It can beseen that the robustness can be slightly improved by ap-plying a variable gain factor adapted to the HVS.

Spatial masking can also be applied if the watermark isembedded in another domain, e.g., DFT, DCT, or DWT.In this case, the nonspatial watermark is first embedded inan image I, resulting in the temporary image I Wt . Thewatermarked image I W is now constructed by mixing theoriginal image I and this temporary image I Wt by meansof a masking image Msk as described above [6], [78]:

I x y Msk x y I x y Msk x y I x yW Wt( , ) ( ( , )) ( , ) ( , ) ( , )= + 1 .

(17)

(a) (b)

20. Watermarking using masking image based on Prewitt operator. (a) Masking imageand (b) difference W x y I x y I x yw( , ) ( , ) ( , )= .

(a) (b)

21. Watermarking where a masking image is used based on DCT-AC energy. (a)Masking image and (b) difference W x y I x y I x yw( , ) ( , ) ( , )= .

Here the masking image must be scaled to values in therange from zero to one. Watermarking methods based onmore sophisticated models for the HVS can be found in[6], [7], [30], [34], [56], [78], [79], [94], [95], [109],and [110].

Extended Correlation-BasedWatermarking TechniquesAnticipating Lossy Compression and FilteringWatermarks that have been embedded in an image bymeans of the spatial watermarking techniques earliercannot be detected reliably after the watermarked imagehas been highly compressed with the lossy JPEG com-pression algorithm. This is due to the fact that such wa-termarks consist essentially of low-power, high-frequency noise. Since JPEG allocates fewer bits to thehigher frequency components, such watermarks can eas-ily be distorted. Furthermore, these watermarks can alsobe affected severely by low-pass operations like linear ormedian filters.

The robustness to JPEG compression can be im-proved in several ways. In [93] the pseudorandom pat-tern W is first compressed and then decompressed usingthe JPEG algorithm. The energy of the resulting patternW is increased to compensate for the energy lostthrough the compression. Finally, this pattern is addedto the image to generate the watermarked image. Theidea here is to use the compression algorithm to filter

out in advance all the energy that would otherwise belost later in the course of the compression. It is assumedthat a watermark formed in this way is invariant to fur-ther JPEG compression that uses the same quality fac-tor, except for small numerical artifacts. Otherpredistortion of the watermark pattern, such as filtering,can be applied to prevent other anticipated degradationof the watermarked image.

In [72] the energy of the watermark pattern is shiftedto the lower frequencies by calculating an individual gainfactor kx y, for each pixel of the watermark pattern insteadof using the same gain factor k for all pixels. First apseudorandom patternW x y( , ) is generated consisting ofthe integers 0 and k. Next, the pattern is divided into 8 8blocks, and the DCT transform W u v( , ) is calculated foreach 8 8 block. The nonzero elements in the 8 8 blocksare now regarded as gain factors kx y, and are adapted insuch a way that the energy in the vulnerable high fre-quency DCT bands FH is minimized (Fig. 23):

{ }= = < <

W u v F u v u vu v F

HH

( , ) , | , .,

2 5 8 5 8

(18)

The energy is minimized under the following con-straints:

W x y k W x y k k kyx

x yyx

x y( , ) ( , ) ,, min , = == ==

1

8

1

8

1

8

1

8

kmax .

(19)

The effect of this high-energy minimization on the wa-termark pattern is illustrated in Fig. 24. Figure 24(a)shows the watermark pattern within an 8 8 block, wherea constant gain factor of k =3 is used. After the high-en-ergy minimization with kmin =0 and kmax =6, the water-mark pattern fades smoothly to zero [Fig. 24(b)]although the sum of the nonzero pixels still equals thesum of the nonzero pixels in the original pattern.

In [35] and [61], JPEG compression immunity is ob-tained by deriving a different gain factor k for each 32 32 pixel block based on a lower quality JPEG compressedimage. A 32 32 pseudorandom pattern representing awatermark bit is added to a 32 32 image tile. A copy ofthis watermarked image tile is degraded according to theJPEG standard for which end a relatively low quality fac-tor is used. If the watermark bit cannot be extracted cor-rectly from this degraded copy, the watermark pattern isadded to the image by means of a higher gain factor and anew degraded copy is formed to check the bit. This proce-dure is repeated iteratively for each bit until all bits can beextracted reliably from the degraded copies. A watermarkformed in this way is resistant to JPEG compression usinga quality factor equal to or greater than the quality factorused to degrade the copies. In Fig. 25 an example of sucha watermark is shown, amplified for visibility purposes.


% B

it E

rror

s

60

50

40

30

20

10

00 10 20 30 40 50 60 70 80 90 100

Qjpeg

W kfixed =2

Wvar

P=32 32, Prefilter Applied before DetectionFedge

22. Influence of a variable gain factor adapted to the HVS onthe robustness of a watermark.

FH

v

u

8 8 DCT

23. DCT bands FH in which the watermark energy is mini-mized.

Anticipating Geometrical TransformsA watermark should not only be robust to lossy compres-sion techniques, but also to geometrical transformationssuch as shifting, scaling, cropping, rotation, etc. Geomet-rical transforms hardly affect the image quality, but theydo make most of the watermarks that have been embed-ded by means of the techniques described in the previoussections undetectable for the watermark detectors. Sincegeometrical transforms typically affect the synchroniza-tion between the pseudorandom pattern of the water-mark and the watermarked image, the synchronizationmust be retrieved before the detector performs the corre-lation calculations.

The most obvious way to achieve shift invariance is us-ing the DFT amplitude modulation technique. If, forsome reason, another watermarking embedding domainis preferred and shift invariance is required, a marker canbe added in the spatial domain to determine the transla-tion. This marker can be a pseudorandom pattern like thewatermark itself. The detector first determines the spatialposition of this marker by shifting the marker over all pos-sible locations in the image and calculating the correlationbetween the marker and the corresponding image part.The translation with the highest correlation defines thespatial position of the marker. Finally, the image is shiftedback to its original position and the normal watermarkingdetection procedure is applied.

An exhaustive search for a marker is computationallyquite demanding. Therefore, in [53] a different approachis proposed: adding a pseudorandom pattern twice, butat different locations in the image. The content of the wa-termark, i.e., the watermark bits, is embedded here in therelative positions of the two watermark patterns. To de-tect the watermark, the detector computes the phase cor-relation between the image and the watermark patternusing the fast Fourier transform (FFT) and it detects the

two correlation peaks of the two patterns. The content ofthe watermark is derived from relative position of thepeaks. If the whole image is shifted before detection, theabsolute positions of the correlation peaks will change,but the relative positions will remain unchanged, leavingthe watermark bits readable for the detector.

In [30] a method is proposed to add a grid to an imagethat can be used to scale, rotate, and shift an image back toits original size and orientation. The grid is representedby a sum of sinusoidal signals, which appear as peaks inthe FFT frequency domain. These peaks are used to deter-mine the geometrical distortion.

In [59] a method is proposed which embeds apseudorandom pattern multiple times at different loca-


1 12 23 34 45 56 67 78 81 1

2 23 3

4 45 5

6 67 7

8 80 0

1 1

2 2

3 3

4 4

5 5

6 6

(a) (b)

24. (a) Original watermark block and (b) low frequency watermark block.

25. Watermark where the local gain factor per block is basedon a lower quality image.

tions in the spatial domain of an image. The detector esti-mates the watermark W by applying a high pass filterFHP to the watermarked image

W I F

F

W HP

HP

=

=

0 0 0 1 0 0 00 0 0 1 0 0 00 0 0 1 0 0 01 1 1 12 1 1 1

0 0 0 1 0 0 00 0 0 1 0 0 00 0 0 1 0 0 0

12

/ .

(20)

Next, the autocorrelation function of the estimatedwatermark W is calculated. This function will have peakvalues at the center and the positions of the multiple em-bedded watermarks. If the image has undergone a geo-metrical transformation, the peaks in the autocorrelationfunction will reflect the same transformation and henceprovide a grid that can be used to transform the imageback to its original size and orientation.

In [40], [41], [86], [74], [87], and [88] a method isproposed that embeds the watermark in a rotation, scale,and translation invariant domain using a combination ofDFT and a log polar map (LPM). Figure 26 presents ascheme of this watermarking method.

First the amplitude of the DFT is calculated to obtain atranslation invariant domain. Next, for every point ( , )u vof the DFT amplitude a corresponding point in the LPM( , ) is determined:

u e v e= = cos( ) sin( ). (21)

This coordinate system of the LPM converts rotationand scaling into translation along the horizontal and ver-tical axis. By taking the amplitude of the DFT of thisLPM, we obtain a rotation, scale, and translation invari-ant domain. In this domain a CDMA watermark can beadded, for instance by modulating the coefficients using(10).

Figure 27 demonstrates an example of the propertiesof the LPM. Part (b) shows the LPM of the Lena image(a). Part (c) depicts a rotated and scaled version of theLena image, and (d) shows its corresponding LPM. Itcan clearly be seen that the rotation and scaling in theoriginal spatial domain are converted into translations inthe LPM domain.

In practice implementing the watermarking scheme il-lustrated in Fig. 26 has been proven to be difficult. Theauthors therefore propose a different approach, where aCDMA watermark is embedded in the translation invari-ant amplitude DFT domain. To make the watermarkscale and rotation invariant, they embed a second water-mark, a template, in this domain. To extract the water-mark, they first determine the scale and orientation of thewatermarked image by using the template in the follow-ing way: The DFT of the watermarked image is calculated. The LPM of the DFT amplitudes and the template pat-tern is calculated. The horizontal and vertical offsets between the twoLPMs are calculated using exhaustive search andcross-correlation techniques, resulting in a scale and rota-tion factor.


Rotation, Scale, and Translation Invariant WM

DFT

DFT

IDFT

IDFT

LPM ILPM

Image

Phase

Phase

Amplitude

Amplitude

26. Rotation, scale, and translation invariant watermarkingscheme.

(a) (b) (c) (d)

27. Example of the properties of the LPM. (a) Original image, (b) LPM of (a), (c) scaled and rotated, and (d) LPM of (c).

Next, the image is transformed back to its original sizeand orientation, and the information-carrying watermarkis extracted.

Correlation-Based WatermarkingTechniques for MPEGIn real-time watermarking applications, robustness is notthe only factor that plays an important role. The other fac-tor that plays a very important role is computational com-plexity. In general, image or video data is transmitted inJPEG or MPEG compressed form. Real-time watermarkembedding must take into account this compressed form,because first decompressing the data, adding a watermarkand then recompressing the data is computationally toodemanding. Therefore, it is desirable to develop water-marking techniques that can operate directly on the com-pressed bit stream, the code words, or the DCT trans-formed coefficients because then it is not necessary tofully decompress and recompress the data. In this sectionwe discuss two such methods for MPEG video streams.Other methods that also operate on code words and DCTcoefficients are discussed in upcoming sections.

In [111] a method is proposed that adds a DCT trans-formed pseudorandom pattern directly to the DC-DCTcoefficients of an MPEG compressed video stream. Thewatermarking process only takes the luminance values ofthe I-frames into account. To embed a watermark the fol-lowing procedure is performed: First a pseudorandompattern consisting of the integers {1,1} is generatedbased on a secret a key. This pattern has the same dimen-sions as the I-frames. Next, the pattern is modulated by awatermark bit string and multiplied by a gain factor.Finally, the 8 8 block DCT transform is applied on themodulated pattern and the resulting DC-coefficients areadded to the corresponding DC-values of each I-frame.The watermark can be detected using correlation tech-niques in the DCT domain or in the spatial domain as de-scribed earlier.

The authors report that the algorithm decreases the vi-sual quality of the video stream drastically. Therefore, thegain factor of the watermark has to be chosen to be verylow (> 100,000) tomaintain reasonable visual quality for the resulting videostream. This is mainly due to the fact that thewatermark pattern is embedded in just one ofthe 64 DCT coefficients, the DC-component.Furthermore, the pattern consists only of lowfrequency components to which the human eyeis quite sensitive. For comparison, the algo-rithm used to embed multiple bits using thecorrelation technique described earlier uses again factor of two and about 1000 pixels perwatermark bit.

In [36]-[39] and [115] a more sophisticatedwatermarking algorithm is proposed that em-beds a watermark not only in the DC-coeffi-

cients, but also in the AC-coefficients of each I-, P-, andB-frame. The watermark here is also a pseudorandompattern consisting of the integers {1,1} generated basedon a secret key. This pattern has the same dimensions asthe video frames. The pattern is modulated by a water-mark bit string and multiplied by a gain factor k.

To embed the watermark, the watermark patternW x y( , ) is divided into 8 8 blocks. These blocks aretransformed to the DCT domain and denoted byW u vx y, ( , ), where x y, , , ,...=0 8 16 and u v, ,...,=0 7. Next,the 2-D blocks W u vx y, ( , ) are reordered in a zig-zag scanfashion and become arrays W ix y, ( ), where i =0 63,..., .W x y, ( )0 represents the DC-coefficient and W x y, ( )63 de-notes the highest frequency AC-coefficient of a 8 8 wa-termark block. Since the corresponding MPEG encoded8 8 video content blocks are encoded in the same way asI ix y, ( ), these arrays can directly be used to add the water-mark. For each video block I ix y, ( ) out of an I-, P-, orB-frame the following steps are performed:

1. The DC-coefficient is modulated as follows:

I I WW x y x yx y, ( ) ( ) ( ), ,0 0 0= + (22)

which means that the average value of the watermarkblock is added to the average value of the video block.

2. To modulate the AC-coefficients the bit stream ofthe encoded video block is searched VLC-by-VLC for thenext VLC code word, representing the next nonzeroDCT coefficient. The run and level of this code word aredecoded to determine its position i along the zig-zag scanand its amplitude I ix y, ( ).

A candidate DCT coefficient for the watermarkedvideo block is generated, which is defined as

I i I i W i iW x y x yx y, ( ) ( ) ( ), ., ,= + 0 (23)

Now the constraint that the video bit rate may not beincreased comes into play. The size SzI of the VLCneeded to encode I ix y, ( ) and the size SzIW of the VLCneeded to encode I iW x y, ( ) are determined using theVLC-Tables B.14 and B.15 of the MPEG-2 standard[47]. If the size of VLC encoding the candidate DCT co-efficient is equal or smaller than the size of the existingVLC, the existing VLC is replaced. Otherwise the VLC is


DriftCalculation

Coefficient Domain WatermarkingWatermark Embedding

VLD TD DQ Q TC VLC

DCT

MPEGDecoder

MP

EG

Vid

eo

MP

EG

Vid

eo

28. Increase of complexity due to drift compensation.

left unaffected. This means that the DCT coefficientI ix y, ( ) is modulated in the following way:

If thenelse

Sz Sz I i I i W iI i

I I W x y x y

W

W x y

x y

= +,

,

( ) ( ) ( )( )

, ,

= I ix y, ( ). (24)

This procedure is repeated until all AC-coefficients of theencoded video block are processed.

To extract the watermark information, the MPEG en-coded video stream is first fully decoded and the water-mark bits are retrieved by correlating the decoded frameswith the watermark patternW x y( , ) in the spatial domainusing the standard techniques.

A major problem of directly modifying DCT-coeffi-cients in an MPEG encoded video stream is drift or erroraccumulation. In an MPEG encoded video stream predic-tions from previous frames are used to reconstruct the ac-tual frame, which itself may serve as a reference for futurepredictions. The degradation caused by the watermark-ing process may propagate in time and may even spatiallyspread. Since all video frames are watermarked, water-marks from previous frames and from the current framemay accumulate and result in visual artifacts. Therefore, adrift compensation signal Dr must be added. This signalmust be equal to the difference of the (motion compen-sated) predictions from the unwatermarked bit streamand the watermarked bit stream. Equation (23) changesfor a drift compensated watermarking scheme into

I i I i W i Dr iW x y x y x yx y, ( ) ( ) ( ) ( )., , ,= + + (25)

A disadvantage of this drift signal is that the complex-ity of the watermark embedding algorithm increases sub-stantially, since an additional DCT operation and acomplete MPEG decoding step are required to calculatethe drift compensation signal. The increase in complexitycompared to the coefficient domain methods is illustratedin Fig. 28.

Due to the bit-rate constraint, only around 10-20% ofthe DCT coefficients are altered by the watermark em-bedding process, depending on the video content and thecoarseness of the MPEG quantizer. In some cases, espe-cially for very low bit-rate video, only the DC-coefficientsare modified. This means that only a fraction of the water-mark pattern W x y( , ) can be embedded, typically around0.5 ... 3% [115]. Since only existing (nonzero) DCT co-efficients of the video stream are watermarked, the em-bedded watermark is video content dependent. In areaswith only low-frequency content, the watermark auto-matically consists of only low frequency components.This complies with the HVS. The watermark energy is

mainly embedded in areas containing a lot of video con-tent energy.

The authors in [115] report that the complexity of thewatermark embedding process is much lower than thecomplexity of a decoding process followed bywatermarking in the spatial domain and re-encoding. Thecomplexity is somewhat higher than the complexity of afull MPEG decoding operation. Typical parameter set-tings for the embedding are k =1 5,..., for the gain factorof the watermark and P =500 000 1 000 000, ,..., , , for thenumber of pixels per watermark bit, yielding watermarklabel bit rates of only a few bytes per second. The authorsclaim that the watermark is not visible, except in directcomparison to the unwatermarked video, and that thewatermark is robust against linear and nonlinear opera-tions like filtering, noise addition and quantization in thespatial or frequency domain.

Noncorrelation-BasedWatermarking TechniquesLeast Significant Bit ModificationThe simplest example of a spatial domain watermarkingtechnique that is not based on correlation is the LSBmodification method. If each pixel in a gray level image isrepresented by an 8-bit value, the image can be sliced upin eight bit planes. In Fig. 29 these eight bit planes arerepresented for the Lena image, where the upper left im-age represents the most significant bit plane and the lowerright image represents the LSB plane.

Since the least significant bit plane does not contain vi-sually significant information, it can easily be replaced byan enormous amount of watermark bits. More sophisti-cated watermarking algorithms that make use of LSBmodifications can be found in [91], [4], [5], [43], and[33]. These watermarking techniques are not very secureand not very robust to processing techniques because theLSB plane can easily be replaced by random bits, effec-tively removing the watermark bits.

MPEG Video Watermarkingby Parity Bit ModificationIn a compressed bit stream we have direct access to thecode words used in the compression algorithm. Similar tothe LSB technique described above, we can embed water-mark in the stream by modifying these code words, yield-ing a computationally efficient watermarking methodwith a high payload [62], [35].

The technique is described as follows. A watermarkconsisting of l label bits b j lj ( , , ,..., )= 0 1 2 1 is embeddedin the MPEG-stream by selecting suitable VLCs and forc-ing the LSB of their quantized level to the value of bj . Toensure that the change in the VLC is perceptually invisi-ble after decoding and that the MPEG-bit stream keeps itsoriginal size, we select only those VLCs for which an-other VLC exists with: the same run length,


The robustness of a watermarkcan be improved by increasingthe energy of the watermark.

a quantized level difference of one, the same code word length.

A VLC that meets this requirement is called a la-bel-bit-carrying-VLC (lc-VLC). According to TablesB.14 and B.15 of the MPEG-2 standard [47], an abun-dance of such lc-VLCs exists. Furthermore, allfixed-length-coded DCT-coefficients following an Es-cape-code meet the requirement. Some examples oflc-VLCs are listed in Table 1, where the symbol s repre-sents the sign-bit. This sign-bit represents the sign of theDCT coefficient level.

The VLCs in the intra- and intercoded macro blockscan be used in the watermarking process. The DC coeffi-cients are not used, because they are predicted from otherDC coefficients and coded with a different set of VLCsand Escape-codes. Furthermore, replacing each DC coef-ficient in intra- and intercoded frames can result in visibleartifacts due to drift. By only taking the AC coefficientsinto account the watermark will adapt itself more to thevideo content and the drift will be limited.

To add the label bit stream L to an MPEG-video bitstream, the VLCs in each macro block are tested. If anlc-VLC is found and the LSB of its level is unequal to thelabel bit b j lj ( , , ,..., )= 0 1 2 1 , this VLC is replaced by an-other one, whose LSB-level represents the label bit. If theLSB of its level equals the label bit bj the VLC is notchanged. The procedure is repeated until all label bits areembedded. In Fig. 30 an example is given of thewatermarking process, where three label bits are embed-ded in the MPEG video stream.

To extract the label bit stream L the VLCs in eachmacro blocks are tested. If an lc-VLC is found, the valuerepresented by its LSB is assigned to the label bit bj . Theprocedure is repeated for j l= 0 1 2 1, , ,..., until nolc-VLCs can be found anymore.

This technique gives a high payload (up to 29 kbit/s)without significant perceptible quality degradation [65].The watermark embedded with this method can easily beremoved by decoding and reencoding the video stream orby relabeling the stream using another randomly gener-ated watermark pattern. This technique can be extendedto make it resistant to relabeling [65], as follows. The wa-termark label bits bi are now not directly stored in theLSBs of the VLCs, but a one-dimensional pseudorandomwatermark patternW x( )is generated consisting of the in-tegers {1,1} based on a secret key, which is modulatedwith the label bits bi . The procedure to add this modu-lated pattern to the video stream is similar to the proce-dure described above.

However, we now select only those VLCs for whichtwo other VLCs exist, with the same run length and thesame codeword length. One VLC must have a level dif-ference of +and the other VLC must have a level differ-ence of . Most lc-VLCs meet these requirements for arelative small (e.g., = 1,2,3). For notational simplicitywe call these pattern-carrying-VLCs (pc-VLCs).

To embed a watermark in a video stream, we add themodulated watermark pattern to the levels of thepc-VLCs. To extract the watermark, we collect thepc-VLCs in an array. The watermark label bits can now beretrieved by calculating the correlation between this arrayof pc-VLCs and the secret watermark pattern W x( ). InFig. 31 an example is given of the watermark embeddingprocess. About 1,000,...,10,000 pc-VLCs are now re-quired to encode one watermark label bit bi and thus dras-tically reduce the payload of the watermark. However,several watermark label bit strings can be added withoutinterfering with each other, if independent pseudoran-dom patterns are used to form the basic pattern W x( ).


29. Bit planes for the Lena image.

DCT Coefficient OrderingIn [55], [114], [54], and [17] a watermarking method isproposed that adds a watermark bit string in the 8 8block DCT domain. To watermark an image, the image isdivided into 8 8 blocks. From these 8 8 blocks theDCT transform is calculated and two or three DCT coef-ficients are selected in each block in the middle band fre-quencies FM (Fig. 32). The selected coefficients arequantized using the default JPEG quantization table [73]and a relatively low JPEG quality factor. The selected co-efficients are then adapted in such a way that their magni-tudes form a certain relationship. The relationshipsamong the selected coefficients compose eight patterns(combinations), which are divided into three groups.Two groups are used to represent the watermark bits 1or 0, and the third group represents invalid patterns. Ifthe modifications which are needed to hold a desired pat-tern become too large, the block is marked as invalid. Forexample, if a watermark bit with value 1 must be embed-ded in a block, the third coefficient should have a lowervalue than the two other coefficients. The embeddingprocess and the list of patterns are represented in Fig. 32.

In Fig. 33 the heavily amplified difference between theoriginal Lena image and the watermarked version isshown. In [13] and [14] a similar watermarking methodis proposed, but here the DCT coefficients are modifiedin such a way that they fulfill a linear or circular constraintimposed by the watermark code.

We note that the techniques described above are simi-lar to the DEW method for real-time MPEG videowatermarking described in the next section.

MPEG Video WatermarkingUsing the DEW AlgorithmThe DEW method is based on selectively discarding highfrequency DCT coefficients in the compressed data

stream. The information bits of the data identifier (label)are encoded in the pattern of DCT blocks in which highfrequency DCT coefficients are removed, i.e., in a patternof energy differences between DCT blocks. For this rea-son, the technique is called a differential energy water-mark (DEW).

The technique is described as follows. The informationthat we wish to embed into the image or video frame isrepresented by the label bit string L consisting of label bitsL j lj ( , ,..., )= 0 2 1 . This label bit string is embeddedbit-by-bit in a set of n 8 8 DCT blocks taken from aJPEG compressed still image or from an I-frame of anMPEG compressed video stream. For the purpose of sim-plicity of the discussion, we will refer to still images andMPEG I-frames as image.

To obtain sufficient robustness, typically n takes onvalues between 16 and 64, which means that a single labelbit is embedded in a region of the image. Before the labelbits are embedded, however, the positions of the 8 8DCT blocks in the image are shuffled randomly as illus-trated in Fig. 34. This shuffling operation, on the onehand, forms the secret key of the labeling algorithm,while on the other hand it spatially randomizes the statis-tics of DCT blocks.

Each bit of the label bit string is embedded in its pri-vate label bit-carrying-region, or lc-region for short, in ashuffled image. For instance, in Fig. 33 the first bit is lo-cated in the top-left-corner of the image in an lc-region ofn =16 DCT blocks. The value of the label bit is encodedby introducing an energy difference between the high fre-quency DCT-coefficients of the top half of the lc-region(denoted by lc-subregion A) containing in this case n/2 =8 DCT blocks, and the bottom half (denoted by lc-subre-gion B) also containing n/2 = 8 DCT blocks. If the lc-sub-region A contains more high frequency energy than thelc-subregion B, the label bit value 0 has been embedded

into the data, and vice versa.To make the determination of high fre-

quency energy easy for images or videoframes that are JPEG or MPEG com-pressed, we compute energies over a subsetof zigzag scanned DCT-coefficients indi-cated by S c( )

{ }S c i i c( ) { , }|( ) .= >0 63 (26)

The zigzag scanned DCT coefficients arenumbered according to Fig. 35. The indexi =0 refers to the DC-coefficient of a DCTblock. The subset of DCT coefficients S c( )over which energies are computed is de-fined by the cut-off index c. The selection of asuitable cut-off index c for an lc-region is es-sential for the robustness and the visibilityof the label bit. The larger the cut-off indexis chosen, the less degradation the label em-bedding will introduce. Here we assumethat we have available a suitable cut-off in-


Table 1. Example of lc-VLCs in Table B.14 of the MPEG-2 Standard.

Variable Length Code VLC size Run Level LSB of Level

0010 0110 s0010 0001 s

8 + 18 + 1

00

56

10

0000 0001 1101 s0000 0001 1000 s

12 + 112 + 1

00

89

01

0000 0000 1101 0 s0000 0000 1100 1 s

13 + 113 + 1

00

1213

01

0000 0000 0111 11 s0000 0000 0111 10 s

14 + 114 + 1

00

1617

01

0000 0000 0011 101 s0000 0000 0011 100 s

15 + 115 + 1

11

1011

01

0000 0000 0001 0011 s0000 0000 0001 0010 s

16 + 116 + 1

11

1516

10

dex c for each lc-region [66]. Note that different lc-re-gions may have different cut-off indexes depending ontheir spatial contents.

The (DCT high frequency) energy E A in lc-subregionA is now defined as follows:

( )E c n QAb

n

i b Qi S c

, , [ ] ./

,( )

jpeg jpeg=

=

0

2 1 2

(27)

Here i b, denotes the non-weighted DCT coefficientwith index i in the bth DCT block of the lc-subregion Aunder consideration. Prior to the calculation of E A , thenotation [] Q jpeg indicates that, the DCT-coefficients arere- or prequantized, in our case using the standard JPEGquantization procedure [73] with quality factor Q jpeg .For embedding label bits into MPEG compressedI-frames a similar approach can be followed, but here, weconfine ourselves to the JPEG notation without loss ofgenerality. The prequantization is done only in determin-ing the cut-off indexes and the calculation of (26), but isnot applied to the actual image data upon embedding the

label. The energy in lc-subregion B, denoted by E B , isdefined similarly.

We now define the energy difference D between thelc-subregions A and B as follows:

( ) ( ) ( )D c n Q E c n Q E c n QA B, , , , , , .jpeg jpeg jpeg= (28)The value of a label bit is encoded as the sign of the en-

ergy difference D. Label bit 0 is defined as D >0and labelbit 1 as D

Salient-Point ModificationIn [82] a watermarking method is proposed that is basedon the modification of salient points in an image. Salientpoints are defined as isolated points in an image forwhich a given saliency function is maximal. These pointscould be corners in an image or locations of high energy,for example.

To embed a watermark we extract the set of pixels withhighest saliency S from the image. Next, a binarypseudorandom pattern W x y( , ) with the same dimen-sions as the image is generated. This can be a line or blockpattern as represented in Fig. 36. If this pattern is suffi-ciently random and covers 50% of all the image pixels,50% of all salient points in set S will be located on the pat-tern and 50% off the pattern W x y( , ). Finally, the salientpoints in set S are adapted in such a way that a statisticallysignificant high percentage of them lies on the watermarkpattern (i.e., the black pixels in the pattern). There aretwo ways to adapt the salient points:

The location of the salient points can be changed bywarping the points towards the watermark pattern. Inthis case small, local geometrical changes are introducedin the image. The saliency of the points can be decreased or increasedby adding well-chosen pixel patterns to the neighbor-hood of a salient point.

To detect the watermark we extract the set of pixelswith highest saliency S from the image and compare thepercentages of the salient points on the watermark pat-tern and off the pattern. If both percentages are about50% no watermark is detected. If there is a statisticallysignificant high percentage of salient points on the pat-tern, the watermark is detected. The payload of this wa-termark is 1 bit.

Fractal-Based WatermarkingSeveral watermark embedding algorithms based onfractal compression techniques have been proposed [24],[80], [8], [9]. They mainly use block-based local iteratedfunction system coding [49]. We first briefly describe thebasic principles of this fractal compression algorithmhere. An image is partitioned at two different resolutionlevels. On the first level, the image is partitioned in rangeblocks of size n n . On the second level the image is parti-tioned in domain blocks of size 2 2n n . For each rangeblock, a transformed domain block is searched for whichthe mean square error between the two blocks is minimal.Before the range blocks are matched on the domainblocks, the following transformations are performed onthe domain blocks.

First, the domain blocks are subsampled by a factor oftwo to get the same dimensions as the range blocks. Sub-sequently, the eight isometries of the domain blocks aredetermined (the original block and its mirrored versionrotated over 0, 90, 180, and 270). Finally, the scale fac-tor and the offset for the luminance values is adapted. Theimage is now completely described by a set of relations foreach range block, by the index number of the best fittingdomain block, its orientation, the luminance scaling, andthe luminance offset. Using this set of relations, an imagedecoder can reconstruct the image by taking any initialrandom image and calculating the content of each rangeblock from its associated domain block using the appro-priate geometric and luminance transformations. Takingthe resulting image as initial image one repeats this pro-cess iteratively until the original image content is approxi-mated closely enough.

In [80] a watermarking technique is proposed whichembeds a watermark of 32 bits b b bl0 1 1 in an image.The embedding procedure consists of the full fractal en-coding and decoding process as described above, wherethe watermark embedding takes place in the fractal en-coding process. First, the image I x y( , ) is split in two re-gions A x y( , ) and B x y( , ). For each watermark bit bj Urange blocks are pseudorandomly chosen from I x y( , ). If


12345678

FM

v

u1 2 3 4 5 6 7 8

8 8 DCT Block with PossibleLocations for Embedding a Bit

HMH

MLL

HLM

MHH

LML

LHM

LLL

HHH

MMM

Patterns for 1

Patterns for 0

Invalid Patterns

Relationships Among ThreeQuantized DCT Coefficients

H: HighM: MiddleL: Low

32. Watermarking based on adapting relationship betweenthree coefficients.

33. Watermark W x y I x y IW x y( , ) ( , ) ( , )= created by adaptingrelationships between DCT coefficients.

bj equals one, the domain blocks to code the U rangeblocks are searched in region A x y( , ). If bj equals zero, thedomain blocks to code the U range blocks are searched inregion B x y( , ). For range blocks which are not involved inthe embedding process, domain blocks are searched in re-gions A x y( , )and B x y( , ). To extract the watermark infor-mation, we must select and re-encode the U range blocksfor each bit bj . If most of the best fitting domain blocksare found in region A x y( , ), the value 1 is assigned to bitbj , otherwise the bit is assumed to be zero.

In [8] and [9] a watermark is embedded by forcingrange blocks to map exactly on specific domain blocks.The watermark pattern here consists of this specific map-ping. This mapping is enforced by adding artificial localsimilarities to the image. The size of the range blocks maybe chosen to be equal to the size of the domain blocks. InFig. 37 an example is given of this process.

The left image illustrates how a fractal encoder wouldmap the range block Rb18 on domain block Db0 in anunwatermarked image. To embed the watermark, thismapping Db Rb0 18 must for instance be changed toDb Rb0 21 . To force the mapping to this form, a blockRb 21 is generated from block Db0 by changing its lumi-nance values. By adding block Rb to the image, wechange the optimal fractal mapping to its desired formDb Rb0 21 , because the quadratic error between Db0 ,corrected for luminance scale and offset and Rb21 is nowsmaller than the error between Db0 and Rb18 .

To detect the watermark we calculate the optimalfractal mapping between the range blocks and the domainblocks. If a statistically significant high percentage of themappings between range blocks and domain blocksmatch the predefined mappings of the watermark pat-tern, the watermark is detected.

Concluding RemarksThis article has given a state-of-the-art overview of com-mon watermarking techniques. New watermarking tech-niques are invented regularly. Some of the watermarkingtechniques are designed for specific applications, whilethe others are not well established yet but have a great po-tential. For the purpose of completeness we briefly list theprinciples of these watermarking techniques below: For printed images dithering patterns can be adaptedto hide watermark information [98], [19]. Instead of the pixel values, the histogram of an imagecan be modified to embed a watermark [116]. The method proposed in [16] embeds a watermark bymodifying the mean value of the pixels of randomly se-lected blocks in an image. The authors in [10] proposed the so-called textureblock coding in which the watermark is embedded bycopying one image texture block to another area in theimage with a similar texture. Recovering the watermark isachieved by computing the autocorrelation function.This method offers high robustness to any kind of distor-

tion because both image areas are distorted in a similarway. This means that the watermark recovery byautocorrelation will still work. Quantization can be exploited to hide a watermark. In[85] a method is proposed in which the pixel values of animage are first coarsely quantized, before some small ad-aptations are made to the image. To detect these adapta-tions the watermarked image is subtracted from itscoarsely quantized version. In [57] selected wavelet coef-ficients are quantized using different quantizers for wa-termark bits 0 and 1.


Label:

lc-region:16 8 8Blocks

1 8 8Block

lc-Subregion:8 8 8Blocks

(a)

1 0 1 1 0 0 1 0 1 0 1 1 1 0 0 1

(b)

(c)

34. (a) Sample I-frame; (b) block-based randomly shuffledI-frame showing the label-carrying (lc) regions and lc-subre-gions; (c) difference between the original and watermarkedimage showing that the DEW algorithm put the watermark inregions with a lot of spatial details.

Watermarks can also be embedded by using projec-tion-based techniques [96], [2]. In these techniques,the original data (divided into blocks) are projectedinto another direction/subspace. The data here can bethe transform coefficients of the original image. Theprojection direction could be random or image de-pendent. The authors in [2] also show that their pro-posed technique could resist rotation and scaling tosome extent. The concept of self-embedding [101], that is embed-ding important parts of an image (for example, the eyes ofa person) onto the image itself, is important to detect(and if possible recover from) a tampering attack in whicha portion of the image has been altered. In [101] the au-thors proposed a high capacity watermarking techniquethat is capable of detecting tampering and to some extentrecover from it.

In this article we have discussed the most importantclasses of watermarking techniques. The first class com-prises the correlation-based methods. Here a watermarkis embedded by adding pseudorandom

Date post:	01-Oct-2015
Category:	Documents
Upload:	sfaizullahbasha
View:	221 times
Download:	3 times

Waternarking Digital Image and Video Data

Documents