Universal Batch Steganalysis
Final rept. 1 Jan 2013-30 Jun 2014
OXFORD UNIV (UNITED KINGDOM)
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The overall theme of this project was to bring steganalysis into practical use, by proposing methods to identify a guilty user of steganalysis in large-scale datasets such as might be obtained by monitoring a corporate network or social network. Identifying guilty actors, rather than payload-carrying objects, is entirely novel in steganalysis, and we propose new methodologies to rank actors by their level of suspicion, without requiring any training data. We identified that modern so-called rich large-dimensional steganalysis features are not well-suited to unsupervised learning of this type, and developed novels ways to collapse large-dimensional data to reduce noise while retaining most of the evidence. These methods have been evaluated using large-scale experiments in total over a million images tested in well over a billion combinations on images crawled from genuine social networks. We also developed new implementations of existing steganalysis feature extractors, which were necessary for work on such a scale. We also examined a source of difficulty in all kinds of steganalysis mismatch between actors caused by different cameras and post-processing. We proposed ways to mitigate such mismatch. Finally, we proposed a new method for attacking a single stego object by exhausting a secret key.
- Computer Systems