Multi-scale segmentation strategies in PRNU-based image tampering localization

With the rapid development of advanced media technology, especially the popularization of digital cameras and image editing software, digital images can be easily forged without leaving visible clues. Therefore, image forensics technology for identifying the accuracy, integrity, and originality of digital images has become increasingly important. Photo-response non-uniformity (PRNU) noise, a unique fingerprint of imaging sensors, is a valuable forgery detection tool because of its consistently good detection performance. All kinds of forgeries, including copy-move and splicing, can be dealt with in a uniform manner. This paper addresses the problem of forgery localization based on PRNU estimation and aims to improve the resolution of PRNU-based algorithms. Different from traditional overlapping and sliding window-based methods, in which PRNU correlations are estimated on overlapped patches, the proposed scheme is analyzed based on nonoverlapping and irregular patches. First, the test image is segmented into nonoverlapped patches with multiple scales. Second, correlations of PRNU are estimated on nonoverlapped patches to obtain the real-valued candidate tampering probability map for each individual scale. Then, all of the candidate maps are fused into a single and more reliable probability map through an adaptive window strategy. In the final step, the final decision map is obtained by adopting a conditional random field (CRF) to model neighborhood interactions. The contributions of this work include the following: a novel PRNU-based forgery localization scheme using multi-scale nonoverlapping segmentation is proposed for the first time. Furthermore, the adaptive fusion strategy involves selecting the best candidate tampering probability individually for each location in the image. Additionally, the experimental results prove that the proposed scheme can achieve much better detection results and robustness compared with the existing state-of-the-art PRNU-based methods.