Improving Selection-Channel-Aware Steganalysis Features
Tomáš Denemark, Jessica Fridrich, Pedro Comesaña · Electronic Imaging · 2016
Currently, the best detectors of content-adaptive steganography are built as classifiers trained on examples of cover and stego images represented with rich media models (features) formed by histograms (or co-occurrences) of quantized noise residuals. Recently, it has been shown that adaptive steganography can be more accurately detected by incorporating content adaptivity within the features by accumulating the embedding change probabilities (change rates) in the histograms. However, because each noise residual depends on an entire pixel neighborhood, one should accumulate the embedding impact on the residual rather than the pixel to which the residual is formally attributed. Following this observation, in this paper we propose the expected value of the residual L1 distortion as the quantity that should be accumulated in the selection-channel-aware version of rich models to improve the detection accuracy. This claim is substantiated experimentally on four modern content-adaptive steganographic algorithms that embed in the spatial domain.