Information-theoretic Bounds for Digital Image Forensics (ITBDIF)
Digital images and image processing pervade everyday life. This calls for new techniques to verify the authenticity and integrity of digital image data. Both questions pertain to the young research field of digital image forensics. Known methods can detect image forgeries by statistically analyzing traces of the involved image processing operators. However, most known methods are heuristic and their effectiveness is known for laboratory conditions only. Rigorous proofs telling us why and under what conditions a method produces reliable results are rare. Current developments increasingly follow an approach to combine ever more features and resort to machine learning for managing the resulting complexity. This approach, however, promises few fundamental insights into causal relationships and limitations. The proposed project breaks with this approach. It tries to establish upper bounds for the information that can be exploited for image forensics. Emphasis is put on situations where known methods are unreliable. This approach informs us for the first time on whether further refinements of forensic methods - heuristic and theoretically founded ones alike - are promising; or whether no more forensically useful traces exist.