A Comparative Study of Bayesian and Dempster-Shafer Fusion on Image Forgery Detection

With the advent of digital imaging, it has become fairly easy to modify the content of an image in many different ways while leaving no obvious visual clue. This has further challenged many existing image forensic techniques. The techniques which perform well with one specific kind of forgeries still suffer from strong limitations when dealing with realistic tampered images. Therefore, an effective strategy for tampering detection and localization requires the application of fusion technique. Although there have been extensive researches on fusion technique on different fields, there has never been a systematic study about fusion technique in image forensic domain. In this paper, we provide a thorough review on the state-of-the-art of fusion methods applied in tampering image detection and localization domain. We then present a practical comparison of two popular fusion techniques: Bayesian and Dempster-Shafer theory (DST) based fusion. The comparison relies on two applications which leverage the two aforementioned fusion techniques. In the first case, aggregating the decision maps of two forensic approaches: Photo Response Non Uniformity (PRNU) and statistical features based approaches has improved the forgery detection performance on saturated and dark regions of images. In the second case, integrating the decision maps of the forensic approach using demosaicing artifacts and the forensic approach using SIFT descriptors and local color dissimilarity maps has enhanced the detection performance on both copy-moved and copy-pasted forgeries images. Experiments show that the DST based fusion performs better in the first case while the Markov Random Field (MRF) based fusion performs better in the second case. It can be concluded that each technique has its own advantages and the best choice depends on each situation and users' requirements.