A Bayesian image steganalysis approach to estimate the embedded secret message
MM&Sec '05 Proceedings of the 7th workshop on Multimedia and security
A Watermarking Method with a New SS Technique
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Message estimation for universal steganalysis using multi-classification support vector machine
Computer Standards & Interfaces
Transactions on Data Privacy
Evolutionary hidden information detection by granulation-based fitness approximation
Applied Soft Computing
KL-sense secure image steganography
International Journal of Security and Networks
| Hi-index | 35.68 |
We define sequential steganography as those class of embedding algorithms that hide messages in consecutive (time, spatial, or frequency domain) features of a host signal. This work presents a steganalysis method that estimates the secret key used in sequential embedding. Steganalysis is posed as the detection of abrupt jumps in the statistics of a stego signal. Stationary and nonstationary host signals with low, medium, and high signal-to-noise ratio (SNR) embedding are considered. A locally most powerful steganalysis detector for the low SNR case is also derived. Several techniques to make the steganalysis algorithm work for nonstationary digital image steganalysis are also presented. Extensive experimental results are shown to illustrate the strengths and weaknesses of the proposed steganalysis algorithm.