Detection- and Information-theoretic Analysis of Steganography and Fingerprinting

The proliferation of multimedia and the advent of the Internet and other public networks have created many new applications of information hiding in multimedia security and forensics. This dissertation focuses on two of these application scenarios: steganography (and its counter problem, steganalysis), and fingerprinting. First, from a detection-theoretic perspective, we quantify the detectability of commonly used information-hiding techniques such as spread spectrum and distortion-compensated quantization index modulation, and also the detectability of block-based steganography. We devise a practical steganalysis method that exploits the peculiar block structure of block-DCT image steganography. To cope with the twin difficulties of unknown image statistics and unknown steganographic codes, we explore image steganalysis based on supervised learning and build an optimized classifier that outperforms previously proposed image steganalysis methods. Then, from an information-theoretic perspective, we derive the capacity and random-coding error exponent of perfectly secure steganography and public fingerprinting. For both games, a randomized stacked-binning scheme and a matched maximum penalized mutual information decoder are used to achieve capacity and to realize a random-coding error exponent that is strictly positive at all rates below capacity.