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| benchmarking for steganography by kernel fisher discriminant criterion | |
| Huang Wei; Zhao Xianfeng; Feng Dengguo; Sheng Rennong | |
| 2012 | |
| Conference Name | 7th China International Conference on Information Security and Cryptography, Inscrypt 2011 |
| Source | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 113-130 |
| Conference Date | November 30, 2011 - December 3, 2011 |
| Conference Place | Beijing, China |
| Indexed Type | EI |
| ISSN | 0302-9743 |
| ISBN | 9783642347030 |
| Department | (1) Institute of Software Chinese Academy of Sciences Beijing 100190 China; (2) State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing 100029 China; (3) Beijing Institute of Electronic Technology and Application Beijing 100091 China |
| English Abstract | In recent years, there have been many steganographic schemes designed by different technologies to enhance their security. And a benchmarking scheme is needed to measure which one is more detectable. In this paper, we propose a novel approach of benchmarking for steganography via Kernel Fisher Discriminant Criterion (KFDC), independent of the techniques in steganalysis. In KFDC, besides between-class variance resembles what Maximum Mean Discrepancy (MMD)merely concentrated on, within-class variance plays another important role. Experiments show that KFDC is qualified for the indication of the detectability of steganographic algorithms. Then, we use KFDC to illustrate detailed analysis on the security of JPEG and spatial steganographic algorithms. © 2012 Springer-Verlag Berlin Heidelberg.; In recent years, there have been many steganographic schemes designed by different technologies to enhance their security. And a benchmarking scheme is needed to measure which one is more detectable. In this paper, we propose a novel approach of benchmarking for steganography via Kernel Fisher Discriminant Criterion (KFDC), independent of the techniques in steganalysis. In KFDC, besides between-class variance resembles what Maximum Mean Discrepancy (MMD)merely concentrated on, within-class variance plays another important role. Experiments show that KFDC is qualified for the indication of the detectability of steganographic algorithms. Then, we use KFDC to illustrate detailed analysis on the security of JPEG and spatial steganographic algorithms. © 2012 Springer-Verlag Berlin Heidelberg. |
| Keyword | Algorithms Cryptography Steganography |
| Language | 英语 |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/15855 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Huang Wei,Zhao Xianfeng,Feng Dengguo,et al. benchmarking for steganography by kernel fisher discriminant criterion[C],2012:113-130. |
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