AFAN: An Attention-Driven Forgery Adversarial Network for Blind Image Inpainting

Blind image inpainting is a challenging task aimed at reconstructing corrupted regions without relying on mask information. Due to the lack of mask priors, previous methods usually integrate a mask prediction network in the initial phase, followed by an inpainting backbone. However, this multi-stage generation process may result in feature misalignment. While recent end-to-end generative methods bypass the mask prediction step, they typically struggle with weak perception of contaminated regions and introduce structural distortions. This study presents a novel mask region perception strategy for blind image inpainting by combining adversarial training with forgery detection. To implement this strategy, we propose an attention-driven forgery adversarial network (AFAN), which leverages adaptive contextual attention (ACA) blocks for effective feature modulation. Specifically, within the generator, ACA employs self-attention to enhance content reconstruction by utilizing the rich contextual information of adjacent tokens. In the discriminator, ACA utilizes cross-attention with noise priors to guide adversarial learning for forgery detection. Moreover, we design a high-frequency omni-dimensional dynamic convolution (HODC) based on edge feature enhancement to improve detail representation. Extensive evaluations across multiple datasets demonstrate that the proposed AFAN model outperforms existing generative methods in blind image inpainting, particularly in terms of quality and texture fidelity.