Diversity-Based Cascade Filters for JPEG Steganalysis

Abstract

Steganalysis is a technique for detecting the existence of secret information hidden in digital media. In this paper, we propose a novel scheme for JPEG steganalysis. In this scheme, we first design the diverse base filters which are able to obtain the image residuals from various directions. Then, we propose a cascade filter generation strategy to construct a set of high order cascade filters from the base filters. We further select the cascade filters with the maximum diversity. The selected filters are convolved with the decompressed JPEG image to obtain residuals which capture the subtle embedding traces. The residuals, termed as the maximum diversity cascade filter residual, are eventually used to extract features to train an ensemble classifier for classification. The experiments are carried out on the detection of stego-images generated using common JPEG steganographic schemes, the results of which demonstrate the effectiveness of the proposed scheme for JPEG steganalysis.