提出可定位扩散模型修复区域的通用指纹技术，解决伪造图像检测难题。
DiffusionPrint: Learning Generative Fingerprints for Diffusion-Based Inpainting Localization
- 通过对比学习捕捉修复区域的统一生成特征指纹
- 在未见修复类型上提升定位准确率最高达28%
- 适用于多种生成模型，适合图像真实性验证场景
基于扩散的修复模型会通过潜在解码器完全重构图像，破坏传统取证方法依赖的相机级噪声模式。本文提出DiffusionPrint，一种基于补丁级别的对比学习框架，学习对潜在解码引入的频谱失真具有鲁棒性的取证信号。该方法利用相同模型生成的修复区域共享一致的生成指纹特性，作为自监督信号。DiffusionPrint采用类MoCo的目标函数，结合跨类别硬负样本挖掘与生成器感知分类头训练卷积主干网络，输出高区分度的取证特征图，可作为融合型图像伪造定位框架中的关键辅助模态。集成至TruFor、MMFusion及轻量级融合基线后，DiffusionPrint在多个生成模型上均实现持续改进，对微调时未见的掩码类型提升高达+28%，并证实对未见生成架构具备泛化能力。代码已开源。
原文摘要 · Abstract (English)
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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