Half-Tone Watermarking Multimedia Security. 2 Outline Half-tone technique Watermarking Method...

transcript

Half-Tone Watermarking

Multimedia Security

Outline

• Half-tone technique

• Watermarking Method

• Measurement

• Robustness

• Conclusion

What is Half-tone?

• Term used in the publishing industry for a black-and-white photograph, indicating the many shades of grey that must be reproduced

• In printing, a continuous tone image, such as a photograph, that has been converted into a black-and-white image

• Halftone images appear routinely in books, magazines, newspapers, printer outputs, and fax documents

Application to Halftone

• Fax machines

• ink jet printers

• Laser printers

• Magazines

• Newspapers

• All of them represent continuous tone images with small dots of one (or a few) colors of ink.

Example-Lena

Halftone technique

• Bit-plane• 1 bit quantizer by medium value• Pseudo-random-noisy binarization• Dithering

– average dithering– random dithering– ordered dithering (1973)

• Error Diffusion (1976)

Average Dithering

• Choosing a certain constant gray level, in particular the average value of image pixels, and using it as a global threshold in deciding whether a pixel should be quantized to 0 or to 1.

• Simple• Quantization contouring is

quite perceptible.

Ordered Dithering

• It compares the pixel intensities with some pseudo-random threshold patterns or screens in order to determine its two-tone output.

0 32 8 40 2 34 10 42

48 16 56 24 50 18 58 26

12 44 4 36 14 46 6 38

60 28 42 20 62 30 44 22

3 35 11 43 1 33 9 41

51 19 59 27 49 17 57 25

15 47 7 39 13 45 5 37

63 31 45 23 61 29 43 21

0 8 2 10

12 4 14 6

3 11 1 9

15 7 13 5

Ordered Dithering-Example

• Pattern ：

• A pixel is “157”– 256/4=64– 64*2 < 157 < 64*3

• Halftone ：

Random Dithering

• Simple and easy to implement– Generate a random

number 1..256; – if it is greater than the

image value, plot the point black

– a lot of "white noise", But free from "artifacts" by digital signal processing

Error Diffusion(1/2)

• It compares the sum of image pixel intensity and error from the past with a fixed threshold to determine the output

• The halftone error is fed forward to its adjacent neighbor using a kernel

• Best quality and slowest

1. find the closest color available

2. Calculate the difference between the value in the image and the color you have

3. divide up these error values and distribute them over the neighboring pixels which you have not visited yet

1. find the closest color available• A pixel is “157”. Xij = vij =157• 157 > 128(Threshold) bi,j = 128

• 157 - 128 = 29

• 29*(7/48) add to next pixel

• …………………………………

Performance

Gray LeverGray Lever AverageAverage RandomRandom OrderOrder DiffusionDiffusion

Error Diffusion-Example(1/2)

112 0 0

0 157 34

232 121 21

112 0 0

0 128 34

232 121 21

1.1. 128 < 157 <256128 < 157 <256 the closest color is 128 is 128

2.2. Calculate the difference Calculate the difference between the value in the between the value in the image and the color you image and the color you havehave157-128 = 29157-128 = 29

Error Diffusion-Example(2/2)

112 0 0

0 128 34

232 121 21

3.3. divide up these error values andivide up these error values and distribute them over the neigd distribute them over the neighboring pixels which you have hboring pixels which you have not visited yetnot visited yet

3/16 5/16 1/16

29*29*

112 0 0

0 128 47

237 130 23

Halftone vs. watermarking

• Copyright, data, or time

• Although there are many existing image data hiding and watermarking methods, most are designed for multi-tone images and could not be applied to halftone image directly

Classification

• Pixel-wise: Change the values of (usually pseudo-randomly chosen) individual pixels (Fu and Au, 2000; Fu and Au, 2002a;)

• Block-wise: Divide the host image into blocks and modify some characteristic of each block.– Alternating between two different dithering matrice

s to halftone a image (Baharav and Shaked, 1998; Hel-Or, 2001; Pei and Guo, 2003)

Data Hiding Watermarking for Halftone Images

Author: Ming Sun Fu, Oscar C. AuSource: IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL.11, NO.4, APRIL 2002

Realignment

• Error correction code

• To help realignment ： – the 4 corner pixels are forced to be zero– A predefined synchronization codeword embe

d in the center portion of the image

M.F. Fu & Oscar C. Au

• Data hiding without original multitone image– Data Hiding Self Toggling (DHST)– Data Hiding Pair Toggling (DHPT)– Data Hiding Smart Pair Toggling (DHSPT)

• Data hiding with original multitone image– Data Hiding Error Diffusion (DHED)– Modified Data Hiding Error Diffusion (MDHED)

Data Hiding - Self Toggling(1/2)

• Encoder1. pseudo-random number generator with a known

seed decide where to be embedded

2. Force the pixel at the location to be either 0 or 255

• Decoder– The same random number generator with the

same seed is used to identify the location

• Extremely simple

• Low perceptual quality– Salt-and pepper noise– A clusters of connected pixels may be formed

Data Hiding - Self Toggling(2/2)

Data Hiding Pair Toggling

• Master pixel ：– A pixel at a pseudo-random location needs to self-t

• Slave pixel ：– One of the neighborhood pixels is chosen randomly

to self-toggle

• Still large salt-and-pepper noise

DHSTDHST DHPTDHPT

Data Hiding by Smart Pair Toggling(1/4)

• Minimizes the connectedness of the cluster

• DHSPT is the same as DHPT except that the choice of the slave pixel for complementary toggling

• A function to Measure how connected the pixel at (m, n) is with neighboring pixels of the same color

Where w(i) = 1 for i = 1, 3, 6, 8 w(i) = 2 for i = 2, 4, 5, 7

x1 x2 x3

x4 x0 x5

x6 x7 x8

10 ),()(),(

iixxfiwnmcon

The con(m,n) is a measure of how connected the The con(m,n) is a measure of how connected the pixel at (m, n) is with neighboring pixels of the sapixel at (m, n) is with neighboring pixels of the same colorme color

2 x0 2

•Maximum con(m,n) = 12 all the same

•If x0 is toggled to and the 8 neighboring are not changed, then

conbefore(m,n) 、 conafter(m,n)-the connection before and after toggling

100 12)(),(),()(

),(),(

afterbefore

iwxxfxxfiw

nmconnmcon

1),(),( 00 ii xxfxxf

• The slave candidate with minimum conafter(m,n) is chosen

• Horizontal or Vertical– conbef(m,n)+conaft(m,n)=10

• Otherwise - conbef(m,n)+conaft(m,n)=11

00 0),(),(

xxfxxf

otherwisenmcon

horizontalveritcalnmcon

DHSPTbefore

DHSPTafter

• Data hiding without original multi-tone image– Data Hiding Self Toggling– Data Hiding Pair Toggling– Data Hiding Smart Pair Toggling

• Data hiding with original multi-tone image– Data Hiding Error Diffusion– Modified Data Hiding Error Diffusion

M.F. Fu & Oscar C. Au

Data hiding with original multi-tone image

• Requirement– Original image is available– Error diffusion

• Start off with DHST

• Using error diffusion to diffuse the self-toggling distortion to many neighboring pixels to achieve higher visual quality

DHED and MDHED

• DHED– DHST is first applied followed by regular error

diffusion– After DHST, error diffusion is applied to the rest of the

pixels• MDHED

– The error diffusion is modified to become non-causal such that the error is fed not only to future pixels but also to past pixels

Data Hiding Error Diffusion

),(),(),(

128),(,255

128),(,0),(

),('),(),(

)1,(7)1,1(3),1(5)1,1(16

jibjivjie

jivjib

jixjixjjv

jiejiejiejiejix

3/16 5/16 1/16

quality measure

• Subjective ：– Typically, halftone quality is measures by j

ust “eyeballing” the original and halftone• Objective

– The human visual system acts like a low-pass filter

– MPSNR

Modified PSNR(1/2)

• Attempt to model the human visual system1. A simple inverse halftone Hlow is generated w

ith a low pass filter (EX ： 5*5 Gaussian low-pass filter)

Original ImageOriginal Image

Halftone ImageHalftone Image

Normal PSNRNormal PSNRcomputationcomputation

41626164

72641267

41626164

Modified PSNR(2/2)

2. Hlow is then fed into the regular PSNR function to generate the MPSNR

– O(x,y) ： the original grayscale image

– Hlow ： halftone image though low-pass filter

– SxSy ： image size

yxOyxHMSE

2),(),(

MSEMPSNR

2255log10

An Extension of DHSPT to color Half-tone Images

Author: Mitsuji, Masayuki Inoue and Yoko KitamuraSource: International Symposium on Communications and Information Technologies 2004(ISCIT 2004)

An Extension of DHSPT to color - 2004

• Extend to color images

• Luminance components are considered into the objective function for deciding the toggling position for visual compensation

• A simple extension– RGB(255,0,255) → (0,255,0)– Low image quality

Color DHSPT-Proposed Method(1/3)

OriginalOriginalImageImage

Y componentY component

HalftoneHalftoneImageImage

EmbedddEmbedddImageImage

y componenty component

YCbCrYCbCrtransformtransform

CompensatedCompensatedImageImage

HalftoningHalftoning DHSTDHST DHSPTDHSPT

|Y-y||Y-y|

1. The embedding positions are decided by the random number and their color components are toggled

2. Only Y component is considered, because of its significance for human visual nature

3. If the Y difference between original and toggled image is small, the effect of toggling is inconspicuous

nmnmnm yYD ,,,

4. The evaluation measure by DHSPT for each component in 3*3 neighbor

5. These measures are combined for the evaluation of appropriate position for toggling as a pair and they given as

conDDB

conDDG

conDDR

Bnmnmnm

Gnmnmnm

Rnmnmnm

otherwisenmcon

horizontalveritcalnmcon

DHSPTbefore

DHSPTafter

nmnmnm yYD ,,,

6.6. Find the minimumFind the minimum

Improved Techniques for Watermarking Halftone Images

Author: Phil Sherry and Andreas SavakisSource:ICASSP-2004

Proposed improvement

• Data Hiding Cell Parity (DHCP)

• Data Hiding Mask Toggling (DHMT)

• Dispersed Pseudorandom Generator (DPRG)

Data Hiding Cell Parity

• Encoding the data stream in the parity domain instead of individual pixel

• Parity is contained within the 2*2 area

• If embedded bit is equal to parity, no action is performed. Otherwise, a complementary toggle is performed

2mod PParityT T T T

T P P T

T T T T

The main motivation to increase DHSPT candidate search space (820)

Data Hiding Mask Toggling

• A post-halftoned watermark that works by encoding two bits in the 2*2 area

• Each 2-bit data is assigned a set of five possible encoding masks

• The masks maintain the cell intensity and minimizing visual degradation

High-Capacity Data Hiding in Halftone Images Using Minimal-Error Bit

Searching and Least-Mean Square Filter

Author: Soo-Chang Pei, Jing-Ming GuoSource: IEEE Transactions on Image Processing, VOL. 15, NO.6,June 2006

Stand EDF flow chat

1. find the closest color available

3 bit gray code and information bit

Generate the Gray code

• Generate the 3-bit Gray code and address each codeword with a corresponding information bit 0 or 1

• The information bits 0 and 1 are arranged alternately, and the table is divided into two groups

Hiding Data

• If the information bit 0 is embedded into the host EDF images, then we check if the binary output vector Bi,j is mapped to 0-group

– If yes, then no bit in this vector should be varied– if no, then we just need to modify one bit in this vector

Proposed high-capacity MEBS data-hiding encoder

Minimal Error Bit Searching(1/2)

• Encoder– Be used for judging which bit in the vector is the most

suitable candidate1. Find the complementary binary vector of the temporary

binary output vector

,1,....,2,1|

jikjiji b

ifbwherenkbB

2. Calculate all the differences between the gray outputs and the complementary binary outputs, where k=1,2,..,n

kji bve ,,,

kjiv ,

Minimal Error Bit Searching(2/2)

3. Suppose ;then, the modified binary output vector is formed as

nkee kji

mji ,....,2,1|min ,,

mjijijiji bbbbB ,,

', ,....,,......,,

•Decoder ：•Using the table lookup method (LUT)•Ex ： from the gray code table, {1,0,1} represents “1” had been embedded

Modified MEBS

• When the capacity gets as high as 50%, the watermarked image quality will be degraded

kji bve ,,,

otherwisebv

bvERRifERR

kjithth

, The best choice is 135

Robust Watermarking

• Divide each original gray-level images of size into several cells of size

• In each cell, embed the same information bit• Use the majority voting to recover the damaged

portion caused by all kinds of distortions

Pei’s Measure

• For an image with size P*Q– Wm,n ： the human visual system (HVS) coeffi

cient at position (m,n)

– Xi,j ： the original image

– Bi,j ： the halftone image

j Rnmnjminmji bwx

QPPSNR

1 1 ,,,,

2255**

LMS’s distribution

Result

• In this paper, a high-capacity data-hiding method in halftone image using the MEBS technique is proposed

• From the experimental results, they prove that, when the capacity is as high as 33.33%, good qualities of the embedded halftone images can be achieved

Conclusion

• Halftone is still dominant in usual office

• challenge– Physical challenges ： scanner resolution– Affine Transformation ： Rotation 、 Translati

on– Dirt and Stains– ……..

Reference

• S.F. Ming and C.A. Oscar, “Data Hiding Watermarking for Halftone Image,” IEEE Trans. on Image Processing, April 2002

• Mitsuji Muneyasu, Masayuki Inoue and Yoko Kitamura, ”An Extension of DHSPT to Color Half-tone Images,” ISCIT 2004

• Phil Sherry and Andreas Savakis, “Improved Techniques for Watermarking Halftone Images,” ICASSP 2004

• Kimiyoshi Arishima, Makoto Kitamura & Fumitaka Ono, “A study on the data hiding rate for halftone images,” Signal Processing and Its Applications, August 2005

• S. C. Pei and J. M. Guo, "High-capacity data hiding in halftone images using minimal error bit searching," IEEE International Conference on Image Processing, Singapore, Oct 2004

• J. M. Guo, S. C. Pei, and H. Lee, "Watermarking in halftone images with parity-matched error diffusion," IEEE International Conference on Speech, Acoustics, and Signal Processing , U.S.A. March 2005

• S. C. Pei, and J. M. Guo, “High capacity data hiding in halftone images using minimal error bit searching and least mean square filter,” IEEE Trans. Image Processing, vol. 15, no. 6, pp. 1665-1679, June, 2006

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