DiffusionPrint: Learning Generative Fingerprints for Diffusion-Based Inpainting Localization

• URL: http://arxiv.org/abs/2604.12443v1
• Date: Tue, 14 Apr 2026 08:30:21 GMT
• Title: DiffusionPrint: Learning Generative Fingerprints for Diffusion-Based Inpainting Localization
• Abstract summary: DiffusionPrint learns a forensic signal robust to the spectral distortions introduced by latent decoding.<n>It exploits the fact that inpainted regions generated by the same model share a consistent generative fingerprint.<n>It consistently improves localization across multiple generative models.
• Score: 11.097006771680896
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern diffusion-based inpainting models pose significant challenges for image forgery localization (IFL), as their full regeneration pipelines reconstruct the entire image via a latent decoder, disrupting the camera-level noise patterns that existing forensic methods rely on. We propose DiffusionPrint, a patch-level contrastive learning framework that learns a forensic signal robust to the spectral distortions introduced by latent decoding. It exploits the fact that inpainted regions generated by the same model share a consistent generative fingerprint, using this as a self-supervisory signal. DiffusionPrint trains a convolutional backbone via a MoCo-style objective with cross-category hard negative mining and a generator-aware classification head, producing a forensic feature map that serves as a highly discriminative secondary modality in fusion-based IFL frameworks. Integrated into TruFor, MMFusion, and a lightweight fusion baseline, DiffusionPrint consistently improves localization across multiple generative models, with gains of up to +28% on mask types unseen during fine-tuning and confirmed generalization to unseen generative architectures. Code is available at https://github.com/mever-team/diffusionprint

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