Forgery-Aware and Edge-Guided Diffusion Model for General and Robust Image Forgery Localization

Image forgery localization, which aims to find suspicious tampered regions by splicing, copy-move, or removal manipulations, has attracted increasing attention. Although considerable progress has been made, most of the existing methods are still far from satisfactory in terms of generalization, e.g., cross-dataset evaluation, and show less robustness against lossy distortion, e.g., post-processing attacks or transmission over online social networks. To address these challenges, a general and robust image forgery localization framework using a diffusion probabilistic model is proposed in this paper. First, aDiffusion-drivenForgery-awareLocalization network (DFL) is presented. In specific, the task of image forgery localization is formulated as the one of mask reconstruction with the forgery-aware features as conditional prior, which introduces forgery-related knowledge into the diffusion process to gradually recover the ground-truth mask from the noisy one. AnEdge-guidedDiffusionRestoration network (EDR) is then developed and integrated into the DFL network, leading to theEDR-DFLmodel, boosting the performance against various post-processing attacks. With EDR-DFL, the distorted forged images could be restored using the EDR model in an edge feature preserving way, which allows the DFL model to localize the tampered regions in the restored images effectively. Extensive experimental results demonstrate that the proposed method significantly outperforms other state-of-the-art methods for cross-dataset evaluation and exhibits superior robustness against various challenging post-processing attacks.