Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Hidden Messages Using Higher-Order Statistics and Support Vector Machines
IH '02 Revised Papers from the 5th International Workshop on Information Hiding
LOCO-I: a low complexity, context-based, lossless image compression algorithm
DCC '96 Proceedings of the Conference on Data Compression
Image steganalysis with binary similarity measures
EURASIP Journal on Applied Signal Processing
Steganalysis by subtractive pixel adjacency matrix
Proceedings of the 11th ACM workshop on Multimedia and security
Using high-dimensional image models to perform highly undetectable steganography
IH'10 Proceedings of the 12th international conference on Information hiding
"Break our steganographic system": the ins and outs of organizing BOSS
IH'11 Proceedings of the 13th international conference on Information hiding
A new methodology in steganalysis: breaking highly undetectable steganograpy (HUGO)
IH'11 Proceedings of the 13th international conference on Information hiding
Breaking HUGO: the process discovery
IH'11 Proceedings of the 13th international conference on Information hiding
Steganalysis of content-adaptive steganography in spatial domain
IH'11 Proceedings of the 13th international conference on Information hiding
IH'04 Proceedings of the 6th international conference on Information Hiding
IH'05 Proceedings of the 7th international conference on Information Hiding
Assessment of steganalytic methods using multiple regression models
IH'05 Proceedings of the 7th international conference on Information Hiding
A novel mapping scheme for steganalysis
IWDW'12 Proceedings of the 11th international conference on Digital Forensics and Watermaking
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It is observed that the co-occurrence matrix, one kind of textural features proposed by Haralick et al., has played a very critical role in steganalysis. On the other hand, the data hidden in the image texture area has been known difficult to detect for years, and the modern steganographic schemes tend to embed data into complicated texture area where the statistical modeling becomes difficult. Based on these observations, we propose to learn and utilize the textural features from the rich literature in the field of texture classification for further development of the modern steganalysis. As a demonstration, a group of textural features, including the local binary patterns, Markov neighborhoods and cliques, and Laws' masks, have been selected to form a new set of 22,153 features, which are used with the FLD-based ensemble classifier to steganalyze the HUGO on BOSSbase 0.92. At the embedding rate of 0.4 bpp (bit per pixel) an average detection accuracy of 83.92% has been achieved. It is expected that this new approach can enhance our capability in steganalysis.