AFTLNet: An efficient adaptive forgery traces learning network for deep image inpainting localization

Deep-learning-based image inpainting repairs a region with visually believable content, leaving behind imperceptible traces. Since deep image inpainting approaches can malevolently remove key objects and erase visible copyright watermarks, the desire for an effective method to distinguish the inpainted regions has become urgent. In this work, we propose an adaptive forgery trace learning network (AFTLN), which consists of two subblocks: the adaptive block and the Densenet block. Specifically, the adaptive block exploits an adaptive difference convolution to maximize the forgery traces by iteratively updating its weights. Meanwhile, the Densenet block improves the feature weights and reduces the impact of noise on the forgery traces. An image-inpainting detector, namely AFTLNet, is designed by integrating AFTLN with neural architecture search, and global and local attention modules, which aims to find potential tampered regions, enhance feature consistency, and reduce intra-class differences, respectively. The experimental results present that our proposed AFTLNet exceeds existing inpainting detection approaches. Finally, an inpainting dataset of 26K image pairs is constructed for future research. The dataset is available at https://pan.baidu.com/s/10SRJeQBNnTHJXvxl8xzHcg with password: 1234.