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

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

ISBN-10: 3642400981

ISBN-13: 9783642400988

ISBN-10: 364240099X

ISBN-13: 9783642400995

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 prepared in topical sections on steganography and steganalysis; watermarking and copyright safeguard; forensics and anti-forensics; reversible facts hiding; fingerprinting and authentication; visible cryptography.

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Extra info for Digital Forensics and Watermaking: 11th International Workshop, IWDW 2012, Shanghai, China, October 31 – November 3, 2012, Revised Selected Papers

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In Section 2, the proposed mapping technology is presented. The set of features used in this work is presented in Section 3. The experimental procedure and results are presented in Section 4. The discussion and conclusions are made in Section 5. 2 Proposed Mapping Technique In this section we first provide some background on residual image and cooccurrence matrix. Then, the proposed mapping technique is presented. 1 Background and Residual Image Co-occurrence matrix was proposed as textural features for image classification in 1973 [16].

99% by using 34,671 features in [13]. Further study and research along this line is our future research in order to further boost steganalysis capability, which means higher steganalyzing capability and lower feature dimensionality. Acknowledgement. This work has been partially supported by National Natural Science Foundation of China (61170271, 31100416) and Fujian Provincial Natural Science Fund of China (2010J01246). References 1. : Steganalysis Using Image Quality Metrics. In: SPIE, EI, Security and Watermarking of Multimedia Content, San Jose, CA (February 2001) 2.

This method excavates the relevance between pixels in the LSB matching stego image from the co-occurrence matrix. This method can acquire high accuracy near to 100% at high embedding rate. In order to increase the accuracy at low embedding rate, we strengthen the differences between the cover image and the stego image to improve the performance of our scheme. Two 8 dimensional feature vectors are extracted separately from the test image and the restoration image, and then the combining 16 dimensional feature vector is used for steganalysis with the FISHER linear classification.

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Digital Forensics and Watermaking: 11th International Workshop, IWDW 2012, Shanghai, China, October 31 – November 3, 2012, Revised Selected Papers by Yane Zhong, Fangjun Huang, Dong Zhang (auth.), Yun Q. Shi, Hyoung-Joong Kim, Fernando Pérez-González (eds.)


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