Proto-LeakNet: Towards Signal-Leak Aware Attribution in Synthetic Human Face Imagery
This addresses the critical challenge of detecting deepfakes and synthetic images for computer vision systems, with incremental improvements in attribution accuracy and robustness.
The paper tackled the problem of source attribution and authenticity verification in synthetic human face imagery by proposing Proto-LeakNet, a signal-leak-aware framework that achieved a Macro AUC of 98.13% and robust performance under post-processing, surpassing state-of-the-art methods.
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 will be available after acceptance.