TEPR-Net: Image Inpainting Localization Network via Texture Enhancement and Progressive Refinement

To counter the security threats posed by the realism of image inpainting generated through diffusion models and GANs, in this paper, we propose a texture enhancement and progressive refinement network (TEPR-Net) for image inpainting localization (IIL). The IIL task is divided into two phases: coarse and fine locating. In the coarse locating phase, we utilize an anomaly texture encoder to capture tampering traces in textures, employ a texture–context feature interaction strategy to effectively integrate texture features with contextual features, and utilize a pixel-level contrastive learning strategy to enhance feature clustering and model generalization. In the fine locating phase, we first enhance the receptive field features in the frequency domain by transforming the features and separately enhancing the low- and high-frequency components. Then, we utilize the coarse localization result to augment the model's sensitivity to tampered regions. Additionally, we introduce a progressive edge distribution guidance and reconstruction strategy that progressively refines the edges of the tampered regions at each level, ultimately generating refined localization results. To support the research and evaluation of the IIL task, we create the Inpaint32K dataset, which is characterized by its large scale, diversity, comprehensiveness, high quality, and authenticity. Finally, extensive experiments demonstrate that TEPR-Net has significant advantages in terms of localization performance, generalizability, extensibility, and robustness.