  Image inpainting, which is the task of filling in missing areas in an image,
is a common image editing technique. Inpainting can be used to conceal or alter
image contents in malicious manipulation of images, driving the need for
research in image inpainting detection. Existing methods mostly rely on a basic
encoder-decoder structure, which often results in a high number of false
positives or misses the inpainted regions, especially when dealing with targets
of varying semantics and scales. Additionally, the absence of an effective
approach to capture boundary artifacts leads to less accurate edge
localization. In this paper, we describe a new method for inpainting detection
based on a Dense Feature Interaction Network (DeFI-Net). DeFI-Net uses a novel
feature pyramid architecture to capture and amplify multi-scale representations
across various stages, thereby improving the detection of image inpainting by
better revealing feature-level interactions. Additionally, the network can
adaptively direct the lower-level features, which carry edge and shape
information, to refine the localization of manipulated regions while
integrating the higher-level semantic features. Using DeFI-Net, we develop a
method combining complementary representations to accurately identify inpainted
areas. Evaluation on five image inpainting datasets demonstrate the
effectiveness of our approach, which achieves state-of-the-art performance in
detecting inpainting across diverse models.
