Digital Forensics and Watermaking: 11th International by Yane Zhong, Fangjun Huang, Dong Zhang (auth.), Yun Q. Shi,

By Yane Zhong, Fangjun Huang, Dong Zhang (auth.), Yun Q. Shi, Hyoung-Joong Kim, Fernando Pérez-González (eds.)

This booklet constitutes the completely refereed post-proceedings of the eleventh overseas Workshop on Digital-Forensics and Watermarking, IWDW 2012, held in Shanghai, China, in the course of October/November 2012. The forty two revised papers (27 oral and 15 poster papers) have been rigorously reviewed and chosen from 70 submissions. The papers are equipped in topical sections on steganography and steganalysis; watermarking and copyright security; forensics and anti-forensics; reversible information hiding; fingerprinting and authentication; visible cryptography.

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Textural Features for Image Classification. IEEE Transactions on Systems, Man and Cybernetics SMC-3(6), 610–621 (1973) 17. : Steganalysis Using High-Dimensional Features Derived from Co-Occurrence Matrix and Classwise Non-Principal Components Analysis (CNPCA). , Jeon, B. ) IWDW 2006. LNCS, vol. 4283, pp. 49–60. Springer, Heidelberg (2006) 18. : A markov process based approach to effective attacking JPEG steganography. , Sallee, P. ) IH 2006. LNCS, vol. 4437, pp. 249–264. Springer, Heidelberg (2007) 19.

E. C h ,C v ,C d and C m , denote the fourth order cooccurrence matrixes along the horizontal, vertical, diagonal and minor diagonal directions respectively. ’s 1(a),(b),(c) and (d),respectively. 22 L. Chen et al. Now the co-occurrence matrix generated by considering four horizontally consecutive elements of a residual image can be represented as follows. C h = |{(ri,j , ri,j+1 , ri,j+2 , ri,j+3 ) |ri,j , ri,j+1 , ri,j+2 , ri,j+3 ∈ [−T : T ]}| (2) where i and j are coordinates in vertical and horizontal two directions, respectively, and T is the threshold.

To facilitate the discussion we classify the residual images used in [13] into the following four types, called R1st ,R2nd ,R3rd and R{hn×n } . The subscripts: 1st, 2nd, and 3rd indicate the order of the filter used to generate the residual image. For instance, R1st indicates such a kind of residual image which have been generated via applying a 1st - order filters. The subscript hn×n represents a different kind of residual images, which are generated via applying a square filter, denoted by hn×n .

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