Robust Image Inpainting Forensics by Using an Attention-Based Feature Pyramid Network

Abstract

Deep learning has injected a new sense of vitality into the field of image inpainting, allowing for the creation of more realistic inpainted images that are difficult to distinguish from the original ones. However, this also means that the malicious use of image inpainting technology to tamper with images could lead to more serious consequences. In this paper, we use an attention-based feature pyramid network (AFPN) to locate the inpainting traces left by deep learning. AFPN employs a feature pyramid to extract low- and high-level features of inpainted images. It further utilizes a multi-scale convolution attention (MSCA) module to optimize the high-level feature maps. The optimized high-level feature map is then fused with the low-level feature map to detect inpainted regions. Additionally, we introduce a fusion loss function to improve the training effectiveness. The experimental results show that AFPN exhibits remarkable precision in deep inpainting forensics and effectively resists JPEG compression and additive noise attacks.