MBNS Steganogram Detection and Payload Capacity Estimation Exploiting the Statistical Moments of Entropy Measure

ABSTRACT Multiple Base Notational System (MBNS) steganography (Zhang & Wang, 2005) scheme employs human vision sensitivity to hide a large amount of secret bits into a still image with a high imperceptibility and is demonstrated to be robust to statistical analysis. In MBNS steganography, secret data are converted into symbols in a notational system with multiple bases. The pixels of a host image are then altered such that their remainders are equal to the symbols, when the pixel values are divided by the bases. Empirically it is observed that the moments of the entropy measure of the remainders in a stego image are larger than that of its clean counterpart. Based on this observation, we propose an active steganalytic approach which effectively breaks the MBNS steganography even with 5% payload capacity, utilizing these moments as features and also makes an estimation of the embedding rate. Experimental results demonstrate that the proposed scheme significantly outperforms prior arts in classification accuracy.

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