Training-Free Image Manipulation Localization Using Diffusion Models

Abstract

Image manipulation localization (IML) is a critical technique in media forensics, focusing on identifying tampered regions within manipulated images. Most existing IML methods require extensive training on labeled datasets with both image-level and pixel-level annotations. These methods often struggle with new manipulation types and exhibit low generalizability. In this work, we propose a training-free IML approach using diffusion models. Our method adaptively selects an appropriate number of diffusion timesteps for each input image in the forward process and performs both conditional and unconditional reconstructions in the backward process without relying on external conditions. By comparing these reconstructions, we generate a localization map highlighting regions of manipulation based on inconsistencies. Extensive experiments were conducted using sixteen state-of-the-art (SoTA) methods across six IML datasets. The results demonstrate that our training-free method outperforms SoTA unsupervised and weakly-supervised techniques. Furthermore, our method competes effectively against fully-supervised methods on novel (unseen) manipulation types.