The growing sophistication of synthetic image and deepfake generation models has turned source attribution and authenticity verification into a critical challenge for modern computer vision systems. Recent studies suggest that diffusion pipelines unintentionally imprint persistent statistical traces, known as signal-leaks, within their outputs, particularly in latent representations. Building on this observation, we propose Proto-LeakNet, a signal-leak-aware and interpretable attribution framework that integrates Closed-set classification with a density-based Open-set evaluation on the learned embeddings, enabling analysis of unseen generators without retraining. Acting in the latent domain of diffusion models, our method re-simulates partial forward diffusion to expose residual generator-specific cues. A temporal attention encoder aggregates multi-step latent features, while a feature-weighted prototype head structures the embedding space and enables transparent attribution. Trained solely on closed data and achieving a Macro AUC of 98.13\%, Proto-LeakNet learns a latent geometry that remains robust under post-processing, surpassing state-of-the-art methods, and achieves strong separability both between real images and known generators, and between known and unseen ones. The codebase is available at the following link: https://github.com/claudiunderthehood/Proto-LeakNet .

翻译：合成图像与深度伪造生成模型的日益精进，使得图像溯源与真实性验证成为现代计算机视觉系统面临的重大挑战。最新研究表明，扩散模型在其输出（特别是潜在表示）中会无意识地留下称为"信令泄露"的持久统计痕迹。基于这一发现，我们提出Proto-LeakNet——一种信令泄露感知的可解释溯源框架，通过将闭集分类与基于密度的开集评估结合于学习到的嵌入空间，无需重新训练即可分析未知生成器。该方法作用于扩散模型的潜在域，通过重新模拟部分正向扩散过程来暴露生成器特定的残余线索。时序注意力编码器聚合多步潜在特征，而特征加权原型头结构则对嵌入空间进行结构化组织，实现透明化的溯源过程。仅使用封闭数据进行训练的Proto-LeakNet取得了98.13%的宏平均AUC值，其学习到的潜在几何结构在经历后处理操作后仍保持鲁棒性，不仅超越了现有最优方法，还在真实图像与已知生成器之间、以及已知与未知生成器之间实现了强分离能力。代码库可通过以下链接获取：https://github.com/claudiunderthehood/Proto-LeakNet