Dynamically Perceived Forgery Conditional Diffusion Model for Scientific Image Tampering Localization

Recently, image tampering localization techniques for scientific publications have attracted increasing attention due to the prevalence of data manipulation and the integrity issue of image content. However, existing methods are still inefficient to expose tampering traces in scientific images due to their unique properties, such as acquisition noise and ambiguous edges. To address these limitations, we propose a Dynamically Perceived Forgery Conditional Diffusion Model, which formulates the prediction of the localization mask as a noise-state aware denoising process. This process progressively localizes the tampered regions by involving time-step guidance to dynamically perceive tampering traces under the variation of diffusion noise, which is jointly controlled by two conditions, including a forgery condition with hierarchically aggregated forensic clues and an enhanced edge condition with multilevel spatial attention. To conduct dynamic controls efficiently, two conditions are fused and then applied to the denoising process via a channel-cross attention module. Furthermore, in the inference stage, a salient element ensemble-based sampling strategy is developed to further improve the reliability against undesired factors of scientific images. Extensive experiments have been conducted on several scientific image tampering datasets, compared with state-of-the-art methods, which demonstrates our superiority in aspects of intra-/cross-dataset evaluations and robustness against post-processing operations.