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Cryptanalysis of Correlation-Based Watermarking Schemes Using Single Watermarked Copy. Author: Tanmoy Kanti Das and Subhamoy Maitra From IEEE SIGNAL PEOCESSING LETTERS, April 2004 Presented by 詹益誌 6/8/2004. Outline. Introduction How to Get Convinced That the Attack is Successful.
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Cryptanalysis of Correlation-Based Watermarking Schemes Using Single Watermarked Copy Author: Tanmoy Kanti Das and Subhamoy Maitra From IEEE SIGNAL PEOCESSING LETTERS, April 2004 Presented by 詹益誌 6/8/2004
Outline • Introduction • How to Get Convinced That the Attack is Successful. • Exact Cryptoanalytic Attack. • Experimental results. • Conclusions
Introduction • Most of the existing digital watermarking techniques are based on correlation between “some information stored in the watermarked copy” and “related information retrieved from attacked watermarked copy”. • They show how to remove this correlation to mount a cipher text-only cryptanalytic attack on these watermarking schemes.
How to Get Convinced That the Attack is Successful • Theorem 1: consider two datasets v1,…,vt and u1,…,ut that are uncorrelated.The mean and standard deviation of the dataset are approximately and
How to Get Convinced That the Attack is Successful • Corollary 1: consider two datasets v1,…,vt and u1,…,ut selected at random from a standard normal distribution. The mean and standard deviation of the data u1-v1,…,ut-vt are approximately
How to Get Convinced That the Attack is Successful • Neither Id nor s(i) is known to the attacker, but some knowledge about statistical distribution of s(i) is known.
Exact Cryptoanalytic Attack • A single watermarked copy I(i) is available. Push I(i) in a stack ST of image. • Take the topmost image from the stack ST and consider it as I#. • The maximum t values of Id# are identified. DCT polynomial are formed. • The coefficients of the DCT polynomial are changed in a small range to create a population of several DCT polynomials and respective images are considered.
Exact Cryptoanalytic Attack 5.From the population, images are selected which are visually indistinguishable from I(i). Moreover, we analyze s(i,#) as mentioned above. If the mean and standard deviation of s(I,#) is close to 0.1 and , respectively, then we select Id# as an attacked one. 6.If required number of images are available, the terminate; otherwise go to step 2.
Experimental results • <image name, count of image, mean, std of the data s(I,#), PSNR(w,a)> • <Lena , 7200, 0.16, 0.69, 33.88> • <Pentagon, 8400, 0.193, 1.71, 32.7> • <Peppers , 6000, 0.081, 1.84, 31.93>
Experimental results • <image name, PSNR(o,w), PSNR(o,a), correlation, similarity factor> • <Lena, 36.77, 33.02, 0.178, 1.08> • <Pentagon, 42.3, 32.1, 0.169, 1.91> • <Peppers, 36.94, 30.78, 0.181, 2.01>
Conclusions • Support the theoretical concepts based on statistical criteria.