SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
IHW '01 Proceedings of the 4th International Workshop on Information Hiding
Detecting Hidden Messages Using Higher-Order Statistics and Support Vector Machines
IH '02 Revised Papers from the 5th International Workshop on Information Hiding
Defending against statistical steganalysis
SSYM'01 Proceedings of the 10th conference on USENIX Security Symposium - Volume 10
Statistically undetectable jpeg steganography: dead ends challenges, and opportunities
Proceedings of the 9th workshop on Multimedia & security
YASS: yet another steganographic scheme that resists blind steganalysis
IH'07 Proceedings of the 9th international conference on Information hiding
Generalised category attack: improving histogram-based attack on JPEG LSB embedding
IH'07 Proceedings of the 9th international conference on Information hiding
IH'04 Proceedings of the 6th international conference on Information Hiding
Category attack for LSB steganalysis of JPEG images
IWDW'06 Proceedings of the 5th international conference on Digital Watermarking
The fractional discrete cosine transform
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing - Part II
Secure steganography using randomized cropping
Transactions on Data Hiding and Multimedia Security VII
IEEE Transactions on Information Theory
Steganalysis using image quality metrics
IEEE Transactions on Image Processing
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Self calibration based blind attacks are quite accurate to detect JPEG domain steganography. Efficient prediction of cover image statistics from its stego version is an important requirement in calibration based attacks. Domain separation i.e. separating embedding domain from the steganalytic domain, is used as a countermeasure against such calibration based attacks because it complicates the cover image prediction process. Most of the domain separation techniques in the recent past are based on randomization of embedding location. In this paper, a new domain separation technique is proposed which is based on parametric discrete cosine transform. In the proposed scheme, image transformation is randomized to separate embedding domain from the steganalytic domain. A comprehensive set of experiments is carried out to justify its applicability and evaluate its performance against JPEG domain steganalyis.