Image Forgery Detection Using Tamper-Guided Dual Self-Attention Network with Multiresolution Hybrid Feature

Image forgery detection can efficiently capture the difference between the tampered area and the nontampered area. However, existing work usually overemphasizes pixel-level localization, ignoring image-level detection. As a result, false detection for tampered image maybe cause a large number of false positives. To address this problem, we propose an end-to-end fully convolutional neural network. In this framework, multiresolution hybrid features from RGB stream and noise stream are firstly fused to learn visual artifacts and compression inconsistency artifacts, which can efficiently identify the tampered images. Furthermore, a tamper-guided dual self-attention (TDSA) module is designed, which can focus the network’s attention on the tampered areas and segment them from the image by capturing the difference between the tampered area and the nontampered area. Extensive experiments demonstrate that compared to existing schemes, our scheme can simultaneously effectively achieve pixel-level forgery localization and image-level forgery detection while maintaining higher detection accuracy and stronger robustness.