Abstract: Image tampering localization aims to detect and locate regions in an image that have been edited or forged.With the rapid proliferation of diffusion models for image generation and editing,even ordinary users can locally modify images with high photorealism via simple textual prompts,posing new challenges to existing forensic techniques.To tackle diffusion-based image editing,this paper proposes a tampering localization method built upon DDIM inversion and multi-step reconstruction consistency.The key observation is that when an edited image undergoes DDIM inversion followed by reconstruction along the denoising trajectory,edited(diffusion-generated) regions can be stably recovered,whereas pristine,unedited regions present noticeable inconsistencies due to their mismatch with the diffusion prior.Leveraging this insight,this paper designs a dual-stream network in which one branch processes the original image and the other one handles a series of reconstructed images obtained at multiple diffusion timesteps.A self-attention mechanism is introduced to explicitly model the dynamic reconstruction behavior across timesteps,while cross-attention is used to fuse domain-specific features from the pristine and diffusion-edited regions.To mitigate data scarcity,it also constructs a diffusion-edited image dataset with ground-truth masks by using several mainstream diffusion models.Experimental results on multiple benchmarks show that the proposed me-thod substantially outperforms existing approaches on both seen and unseen types of images,demonstrating strong robustness and generalization.

Key words: Image tampering localization, Diffusion-based image editing, Image inversion and reconstruction, Forgery image dataset