By Yane Zhong, Fangjun Huang, Dong Zhang (auth.), Yun Q. Shi, Hyoung-Joong Kim, Fernando Pérez-González (eds.)
This publication constitutes the completely refereed post-proceedings of the eleventh foreign 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 geared up in topical sections on steganography and steganalysis; watermarking and copyright defense; forensics and anti-forensics; reversible information hiding; fingerprinting and authentication; visible cryptography.
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Extra resources for Digital Forensics and Watermaking: 11th International Workshop, IWDW 2012, Shanghai, China, October 31 – November 3, 2012, Revised Selected Papers
Example text
1 shows the gray level co-occurrence matrix. GLCM not only reflects the distribution characteristics of the luminance, but also reflects the distribution characteristics of the locations with the same or close luminance, including the comprehensive information about the direction, the adjacent interval, the amplitude of change. , N y − 1 Fig. 1. -q. Guo et al. Def 1: Average co-occurrence matrix defines the average of four GLCMs that 90 o and 135 o , the distance is d . θ Cd = (Cd 0 + Cd 1 + Cd 2 + Cd 4 ) / 4 is 0 o , 45 o (4) Average co-occurrence matrix reflects the change of the pixels in four directions.
LNCS, vol. 6958, pp. 102–117. Springer, Heidelberg (2011) 12. : Textural features for steganalysis. , Ghosal, D. ) IH 2012. LNCS, vol. 7692, pp. 63–77. Springer, Heidelberg (2013) 13. : Kodovsk´ y: Rich Models for Steganalysis of Digital Images. IEEE Trans. on Info. Forensics and Security 7(3), 868–882 (2012) 14. fr 15. : ”Break our steganographic system”: The ins and outs of organizing BOSS. , Ker, A. ) IH 2011. LNCS, vol. 6958, pp. 59–70. Springer, Heidelberg (2011) 16. : Textural Features for Image Classification.
IH 2002. LNCS, vol. 2578, pp. 340–354. Springer, Heidelberg (2003) 3. : Steganalysis of Additive Noise Modelable Information Hiding. Master Thesis of Rensselaer Polytechnic Institute, Troy, New York, advised by Professor W. A. Pearlman (2003) 4. : Steganalysis based on multiple features formed by statistical moments of wavelet characteristic functions. , P´erez-Gonz´ alez, F. ) IH 2005. LNCS, vol. 3727, pp. 262–277. Springer, Heidelberg (2005) 5. : Feature-Based Steganalysis for JPEG Images and its Implications for Future Design of Steganographic Schemes.