A fast and effective steganalytic technique against JSteg-like algorithms
Proceedings of the 2003 ACM symposium on Applied computing
On Estimation of Secret Message Length in JSteg-like Steganography
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
MM&Sec '06 Proceedings of the 8th workshop on Multimedia and security
A Markov process based approach to effective attacking JPEG steganography
IH'06 Proceedings of the 8th 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
Improved detection of LSB steganography in grayscale images
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
Detection of LSB steganography via sample pair analysis
IEEE Transactions on Signal Processing
Secure covert channels in multiplayer games
Proceedings of the 10th 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
Proceedings of the first ACM workshop on Information hiding and multimedia security
| Hi-index | 0.00 |
Model-based steganography is a promising approach for hidden communication in JPEG images with high steganographic capacity and competitive security. In this paper we propose an attack, which is based on coefficient types that can be derived from the blockiness adjustment of MB2. We derive 30 new features to be used in combination with existing blind feature sets leading to a remarkable reduction of the false positive rate (about 10:1) for very low embedding rates (0.02 bpc). We adapt Sallee's model-based approach for steganalysis where the Cauchy model itself is used to detect Cauchy model-based embedded messages. We apply a gradient aware blockiness measure for improved reliability in the detection of MB1. We evaluate our proposed methods based on a set of about 3000 images.