Effective images splicing detection based on decision fusion

Localizing the tampered regions in forgery images is an important and challenging problem in forensics applications. Though there have been an extensive studies on image forgery localization over past decade, each method still has its own limitations. Therefore, it is promising to fuse different forensic approaches in order to obtain better localization performance. In this paper, we propose a framework to aggregate the decision maps of two forensic approaches: Photo Response Non-Uniformity (PRNU) based approach and statistical features based approach using Dempster-Shafer Theory. PRNU noise can be considered as a camera fingerprint thereby being used effectively to localize tampering images. However, the most challenging limitation of this approach is its false identifications on textured, saturated and dark regions. By combining with the statistical feature based approach, we can decrease this false alarm rate on saturated and dark regions. The extensive experimental results demonstrate that the proposed method significantly outperforms the single PRNU based approach.