DiffusionPrint learns robust forensic feature maps via MoCo-style contrastive training on diffusion inpainting fingerprints, boosting localization accuracy by up to 28% when fused into existing IFL systems and generalizing to unseen models.
Using noise inconsisten- cies for blind image forensics.Image and Vision Computing, 27(10):1497–1503, 2009
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DiffusionPrint: Learning Generative Fingerprints for Diffusion-Based Inpainting Localization
DiffusionPrint learns robust forensic feature maps via MoCo-style contrastive training on diffusion inpainting fingerprints, boosting localization accuracy by up to 28% when fused into existing IFL systems and generalizing to unseen models.