Cost assignment based adaptive active forensics scheme for image tampers

Abstract

With the rapid development of multimedia editing technology and DeepFake technology, image integrity and authenticity meet more challenges. Most existing methods only focus on improving the accuracy of tamper detection and localization, but ignore the potential tampering risk, which is related to the saliency. There are uneven potential tamper threats to any graphic images, and it is interesting to exploit saliency to adaptively assign embedding cost. We propose an active forensics scheme for tamper localization by adaptively adjusting cost assignment. The experimental results demonstrate a significant improvement in transparency, localization accuracy, and robustness against unintentional attacks.