Steganography is the practice of hiding a secret message in innocent objects such that the very existence of the message is undetectable. Steganalysis, on the other hand, deals with finding the presence of such hidden messages. 'Canvass' is software developed to perform JPEG image steganalysis. This software uses pattern recognizer to classify unknown images into cover (innocent) or stego (containing hidden message). The pattern recognizer, a support vector machine, is trained using the underlying statistical information in the cover and stego images. Some of the popular steganographic algorithms produce double-compressed JPEG images. A blind steganalyzer built on the assumption that it will see only single-compressed images gives misleading results of classification for such images. The goal of the current work is to develop a double-compression detector for JPEG images that extends the existing Canvass software. We develop a double-compression detector based on Partially Ordered Markov Models (POMMs) that can act as a pre-classifier to the blind steganalyzer. We also use the patterns of relative histogram values of the quantized DCT coefficients for improved accuracy of detection. After detecting the double-compression, we carry out cover Vs. stego detection and primary quality factor estimation. We compare our double-compression detector with two other state-of-the-art detectors. Our detector is found to have better performance compared to the state-of-the-art detectors. The current work considers a limited set of quality factors for double-compression but this novel method for steganalysis of double-compressed data looks promising and could be generalized for any combination of primary and secondary quality factors.
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