A MORE SECURE BLIND DIGITAL WATERMARKING TECHNIQUE USING 3- LEVEL DWT Preeti Arya 1 Maharana Pratap College of technology, Gwalior [email protected]1 Dhirendra Singh Tomar 2 Maharana Pratap College of technology, Gwalior 2 Abstract— Digital watermarking is the process of hiding information which is used to hide proprietary information in digital media like image, audio, video. In digital watermarking the wavelet based transformation technique is an effective in image analyzing and processing. Thus the color-image watermark algorithm based on discrete wavelet transformation (DWT) begins to draw an increasing attention. In Proposed paper, we will improve the value of PSNR as compare to the previous paper and the outcome value of PSNR will be improved up to 50%. Keywords— DWT; DCT; DFT; LSB. I. INTRODUCTION (HEADING 1) In the Last few years, digital watermarking technology is an emerging field in computer science, cryptography, signal processing and communications. Digital watermarking is the process of hiding information which is used to hide proprietary information in digital media like photographs, digital music, or digital video [1-2]. The ease with which digital content can be exchanged over the Internet has created copyright infringement issues. Copyrighted material can be easily exchanged over peer-to-peer networks, and it has caused major concerns to content providers engaged in producing the contents. For an efficient watermarking method, it should be robust to compression, filtering, rotation, scaling cropping, and collusion attacks among many other digital processing operations. Digital image watermarking techniques can be categorized into two domain namely spatial domain Watermarking and Transform Domain Watermarking techniques. In comparison with spatial domain techniques [3], transform-domain watermarking techniques (DWT) are generally more effective in terms of the imperceptibility and robustness requirements of digital watermarking algorithms [4-5]. A transform domain technique is proposed which shows greater robustness to common signal distortions. The main advantage of the proposed wavelet-based technique lies in the method used to embed the watermark in low frequency band using blending technique. The Performance of DWT-based digital image watermarking algorithms could be improved by increasing the level of DWT. II. APPLICATIONS OF DIGITAL IMAGE WATERMARKING There are diverse applications of image watermarking. These are listed as follows [6]. A. Copyright Protection When a new work is produced, copyright information can be inserted as a watermark. In case of dispute of ownership, this watermark can provide evidence.
Transcript
A MORE SECURE BLIND DIGITALWATERMARKING TECHNIQUE USING 3-
Abstract— Digital watermarking is the processof hiding information which is used to hideproprietary information in digital media likeimage, audio, video. In digital watermarkingthe wavelet based transformation technique isan effective in image analyzing andprocessing. Thus the color-image watermarkalgorithm based on discrete wavelettransformation (DWT) begins to draw anincreasing attention. In Proposed paper, wewill improve the value of PSNR as compare tothe previous paper and the outcome value ofPSNR will be improved up to 50%.
Keywords— DWT; DCT; DFT; LSB.
I. INTRODUCTION (HEADING 1)In the Last few years, digitalwatermarking technology is an emergingfield in computer science, cryptography,signal processing and communications.Digital watermarking is the process ofhiding information which is used to hideproprietary information in digital medialike photographs, digital music, ordigital video [1-2]. The ease with whichdigital content can be exchanged over theInternet has created copyrightinfringement issues. Copyrighted materialcan be easily exchanged over peer-to-peernetworks, and it has caused majorconcerns to content providers engaged inproducing the contents. For an efficientwatermarking method, it should be robustto compression, filtering, rotation,scaling cropping, and collusion attacks
among many other digital processingoperations. Digital image watermarkingtechniques can be categorized into twodomain namely spatial domain Watermarkingand Transform Domain Watermarkingtechniques. In comparison with spatialdomain techniques [3], transform-domainwatermarking techniques (DWT) aregenerally more effective in terms of theimperceptibility and robustnessrequirements of digital watermarkingalgorithms [4-5]. A transform domaintechnique is proposed which shows greaterrobustness to common signal distortions.The main advantage of the proposedwavelet-based technique lies in themethod used to embed the watermark in lowfrequency band using blending technique.The Performance of DWT-based digitalimage watermarking algorithms could beimproved by increasing the level of DWT.
II. APPLICATIONS OF DIGITAL IMAGE WATERMARKING
There are diverse applications of image watermarking. These are listed as follows[6].
A. Copyright ProtectionWhen a new work is produced, copyright
information can be inserted as awatermark. In case of dispute ofownership, this watermark can provideevidence.
B. Broadcast MonitoringThis application is used to monitor
unauthorized broadcast station. It canverify whether the content is reallybroadcasted or not.
C. Tamper DetectionFragile watermarks are used for tamper
detection. If the watermark is destroyedor degraded, it indicates presence oftampering and hence digital contentcannot be trusted.
D. Authentication and Integrity VerificationContent authentication is able to
detect any change in digital content.This can be achieved through the use offragile or semi-fragile watermark whichhas low robustness to modification in animage.
E. FingerprintingFingerprints are unique to the owner of
digital content and used to tell when anillegal copy appeared.
F. Content DescriptionThis watermark can contain some
detailed information of the host imagesuch as labeling and captioning. For thiskind of application, capacity ofwatermark should be relatively large andthere is no strict requirement ofrobustness.
G. Covert CommunicationIt includes exchange of messages
secretly embedded within images. In this case, the main requirement is that hiddendata should not raise any suspicion that a secret message is being communicated.
III. PROPERTIES OF DIGITAL WATERMARKING
A. RobustnessRobustness defines how much the noise
or attack is tolerable in the system.Watermark should be more robust. It means
it is impossible to remove the watermarkfrom the watermarked video or image. Itcan be but with the sufficient knowledgeof embedding process [8].
B. SecurityAuthentication of Watermark should be
exceptionally well so that nounauthorized party could detect thewatermark even after recognizing theexact embedding and extraction algorithms[7].
C. UnambiguousThe Copyright owner of the content
should be distinctively identified by theretrieved watermark, or in case offingerprinting applications, theauthorized recipient of the contentshould be specifically recognized [9].
D. ImperceptibilityImperceptibility means impossible or
difficult to perceive by the mind orsenses. The watermark embedded into thedigital video sequence should beindiscernible to Human Vision System(HVS) [8].
E. IrremovableIt must be difficult or impossible for
a hacker to remove the watermark withoutperceptibly demeaning the originalsignal. Detection of watermark bycomparing several watermarked signalsbelonging to the same author should beimpossible for a pirate.
F. Computational CostDifferent applications entail the
detection to work and embedding atdifferent speeds. Embedding and detectionought to work in real time in broadcastmonitoring, thus they have to be ratherfast and be supposed to have lowcomputational complexity [7].
G. LoyaltyA watermark is considered to be having
a high reliability if it is difficult fora viewer to recognize the degradationcaused by it [8].
IV.DIGITAL WATERMARKING TECHNIQUESThe Digital watermarking techniques are
categorized into two different domains:pixel domain or spatial domain andtransform domain or frequency domain.
A. Spatial DomainIn this technique, watermark is
embedded by directly amending the pixelvalues of the host image/video. Theforemost advantages of pixel basedmethods are that they are conceptuallysimple having very low computationalintricacies. These methods are,therefore, commonly used in videowatermarking where the prime concern isreal-time performance [9]. The resultingwatermark may or may not be perceptible,depending upon the intensity value. Forexample picture cropping, commonly usedby image editors, can be used to removethe watermark [10]. Some methods ofwatermarking in spatial domains are:
1) Correlation based techniques: In thistechnique, the watermark W(x, y) is addedto the original content O(x, y) accordingto the equation (1).
Ow (x, y) = O(x, y) + kW(x, y)In equation (1), k is a gain factor andOw is the watermarked content. As weincrease the value of k, it will expensethe quality of watermarked contents.
2) Least Significant Bit Modification (LSB): LeastSignificant Bit modification (LSB) is thesimplest technique of this domain. Inthis method, the watermark is justembedded into the least significant bitsof the original video or flips the LSB.Though it is the most popular scheme dueto its simplicity, but has somelimitations like incompetence in dealingwith a range of attacks, poor quality of
the produced video, least robustness andlack of imperceptibility [8].B. Frequency Domain
In such techniques, embedding ofwatermark is done by altering thetransform coefficients of the frames ofthe video sequence. Discrete FourierTransform (DFT), Discrete CosineTransform (DCT), and Discrete WaveletTransform (DWT) are the most commonlyused transforms. The watermark isembedded evenly in overall domain of anoriginal data. Initially, the hostimage/video is converted into frequencydomain by transformation techniques.Thereafter, the transformed domaincoefficients are changed to store thewatermark information. Ultimately, thewatermarked image/video is obtained byapplying the inverse transform. Due toits multi resolution characteristics, anumber of researches concentrated onusing DWT [1]. It provides both spatialand frequency domain characteristics thusmaking it compatible with the HumanVisual System (HVS). Furthermore, DWT canbe combined with other algorithms toenhance robustness and invisibility. Thetransforms that comes under frequencydomain are as follows:
1)Discrete Fourier Transform (DFT): FourierTransform (FT) is a process whichtransforms a continuous function into itsfrequency components. The correspondingtransform requires the Discrete FourierTransform (DFT) for discrete valuedfunction. In digital image processing,the even functions that are non periodiccan be defined as the integral of sineand/or cosine multiplied by a weighingfunction. This weighing functionformulates the coefficients of theFourier transform of the signal. FourierTransform grants examine and processingof the signal in its frequency domain bymeans of analysing and modifying thesecoefficients. DCT having two parts ofwatermarking, one is a template whichdoes not include any information initself but is able to detect any
transformations undergone by the image,and another one is a spread spectrummessage containing hidden information.The length of the hidden information isassumed to be short and it is deal withthe pre-processing algorithm to constructthe new message of length. The luminancecomponent of the cover image is extractedand is used to calculate the DFTcoefficients prior to embedding thehidden message. Thereafter, the hiddendata and the template are embedded inthese coefficients [11]. The template isembedded along two lines in the coverimage which go through the origin. Theidea is to detect anyattacks/transformation the image hasundergone.
2) Discrete Cosine Transform (DCT): DCT isvastly used method in image watermarkingand provides accurate result. DCT isfaster and can be implemented in O (n logn) operations. In Discrete CosineTransform, images get decomposed intodifferent frequency bands, and we mainlyfocused on middle frequency band. Inthis, watermark information is easilyembedded into the middle frequency band.The middle frequency bands are chosen toavoid the most visual important parts ofthe image which are of low frequencywithout revealing themselves toelimination through compression and noiseattacks. DCT is important method forvideo processing. It also gives accurateresult in video watermarking and resistsvarious attacks. Another advantage of DCTis that it breaks a video frame is intodifferent frequency band which enables itto easily embed watermarking informationinto the middle frequency bands of avideo frame [12]. DCT not only improvesthe peak signal to noise ratio but isalso more robust against various attackslike frame dropping and frame averaging.
3) Discrete wavelet Transform (DWT): Discretewavelet transform (DWT) is based on smallwaves, called wavelets. It is amathematical tool for hierarchicallydecomposing an image. Non stationary
signals can be processed by DWT. Bothfrequency and spatial description of animage provided by the wavelet transform.Temporal information is retained in thistransformation process unlikeconventional Fourier transformtranslations and dilations of a fixedfunction created the wavelets calledmother wavelet. This section analyze thesuitability of DWT for image watermarkingand represent the advantages of using DWTas against other transform. Apply DWTcorresponds to processing the image for2D images by 2D filters in eachdimension. Input image is divided by thefilters into four non overlapping multi-resolution sub-bands LL1, LH1, HL1 andHH1. The sub- band LL1 is different fromother sub-bands in terms of scale [13].
The LL1 represents the Coarse-scaleDWT coefficients while the sub-bands LH1,HL1 and HH1 represent the final scale ofDWT coefficients. As illustrated in Fig.4, for obtaining the next coarse scale ofDWT coefficients, the sub-band LL1 isfurther processed until some final scaleN is reached. When N is reached will have3N+1 sub-bands consisting of multi-resolution sub-bands LLN, LHX, HLX andHHX where X ranges from 1 until N. DWT isappropriate transform to identify theareas in the host image where a watermarkcan be embedded effectively. Most of theimage energy is determined at the lowerfrequency sub-bands LLX and thereforeembedded watermarks in these sub-bandsdegrade the image considerably.Robustness increase significantly whenwatermark is embedded in low frequencysub-bands [13]. On the other hand, thehigh frequency sub bands HHX include theedges and textures of the image. Thehuman eye is not very sensitive topredict the changes in such sub-bands.This allows the watermark to be embeddedwithout being perceived by the human eye.
V. ATTACKS ON DIGITAL WATERMARKINGThere are various possible malicious
intentional or unintentional attacks that
a watermarked matter. The accessibilityof wide range of image processing software’s made it possible to achieveattacks on the robustness of thewatermarking systems. The aim of theseattacks is foil the watermark fromperforming its intended purpose. A briefintroduction to various types ofwatermarking attacks is follows.A. Removal Attack
In this attacks mean to remove thewatermark data from the watermarkedobject [14].
B. Geometric attackAll manipulations that distress the
geometry of the image such as flipping,rotation, cropping, etc. should bedetectable [15].
C. Protocol AttackIn this attacks do neither mean at
destroying the embedded information norat disabling the detection of theembedded information [14].
D. Cryptographic attacksIt is deal with the brilliant of the
security [14].
VI.RELATED WORKMistry [5] introduced digital
watermarking methods- Spatial domain(like LSB) and transform domain (likeDCT, DWT) methods. The spatial domain isthe normal image space, in which a changein position in image directly projects toa change in position in space. Ex.-LeastSignificant bit (LSB) method. TransformDomain method produce high qualitywatermarked image by first transformingthe original image into the frequencydomain by the use of Fourier Transform,Discrete Cosine Transform (DCT) orDiscrete Wavelet transforms (DWT). It wasobserved that transform watermarking is
comparatively much better than thespatial domain encoding. Transform basedmethods are very efficient and havingmore robustness for image processingattacks.
Zhang and Yong Ping [16] proposed anovel watermarking algorithm which usesfeatures of lower frequency sub-band ofwavelet coefficient to limit thepositions for watermarking. The positionsare taken as indexes of points to embedwatermarking and may these positions tomedium frequency space to carrywatermarking.
Thongkor et al., [17] presents imagewatermarking based on DWT coefficientsmodification for social networkingservices. Here decomposition is done onthe blue component of original host imageby the DWT to obtain the coefficients inLL sub-band, and some of them are used tocarry watermark signal.
Deb et al., [18] proposed a combinedDWT and discrete cosine transform baseddigital image watermarking technique forcopyright protection. Here watermarkingbits are embedded in the low frequencyband of each DWT block of selecteddiscrete wavelet transform sub-band.
Sridhar B and Arun C [19] proposedsecure multiple image watermarkingtechniques using DWT with the motivationto maintain the quality of the image inwhich the original image was interlacedinto even and odd rows of images and deinterlaces the two images. Wavelet basedapproach is employed for hiding watermarkimages.
VII. PROPOSED METHODOLOGY
A. Proposed Algorithm1. Consider the image to be resized to
N x N; (Original image I having Nvalue of pixel)
2. Image is to be changed into gray anddouble for 2-D image.
3. Compute low and high pass filter fordecomposition and reconstruction.
4. Perform DWT on the watermark imageto split it into four non-overlapping multi-resolutioncoefficient sets: L_L, L_H, H_L,H_H(where L represent Low, H representHigh) .
5. Perform DWT again on two L_H1 andH_L1 coefficient sets to get eightsmaller coefficient sets and selectfour coefficient sets: L_L1,L_H1,H_L1H_H1.
6. Perform DWT again on fourcoefficient sets: L_L2,L_H2,H_L2,H_H2 to get sixteensmaller Coefficient sets and selectfour coefficient sets: L_L2,L_H2,H_L2,H_H2 and form into 8*8 blocks.
7. Embed the image using above 3-leveldwt coefficients.Perform the inverse DWT (IDWT) onthe DWT transformed image, with themodified coefficient sets, toconstruct the watermarked image.
B. Proposed Technique1. In proposed work we start by
selecting two kinds of image one isfor cover image and another is forwatermark image.
2. Here we apply 3 level DWT transformi.e they combine the cover imageand watermark image.
5. Calculate PSNR & MSE value forEmbedding Process.
6. Calculate PSNR & BES value forExtraction Process.
C. Flow Chart
VIII. RESULT METHODOLOGYThe matrices used areA. Peak Signal to Noise Ratio (PSNR)The PSNR is most commonly used as a measure of the quality of de-speckled image. The PSNR is defined as:
PSNR = 10log10()B. Bit Error Rate (BER)BER is an important factor that is usedfor transmitting digital information fromone position to another. The BER isdefined as:
BER= Number of errors/ Total Number ofbit sent
C. Comparison between Base vs Proposed
IX. CONCLUSIONIn this paper we propose an efficientwatermarking algorithm for digital image.
We implemented a 3-level DWT basedtechnique by which the result isimproved. And the performance of PSNRvalue is increased up to 55% as comparedto previous result.
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