DiffStamp: Differentiably Rendered Proactive Watermark for Visual Disclosure

The rapid proliferation of high-fidelity AI-generated content (AIGC) has made in-content provenance especially important for public disclosure of realistic synthetic media, and potentially also for creator-side intellectual-property (IP) claims. We propose DiffStamp, a proactive visible watermarking framework for addressing them. By integrating a differentiable renderer into diffusion models' inference, DiffStamp jointly optimizes stamp placement and image content through four stages: locating, blending, filtering, and embossing. Experiments on FLUX.1-dev, Qwen-Image-Edit, and HunyuanVideo show model-agnostic applicability, modest overhead, and persistence against automated removal and regeneration attacks. A 108-participant visual-search study further shows that it harmonizes with image content; its stamps are less noticeable than naïve overlays yet discoverable under deliberate search. We further present a concept-aware attribution extension in which designated concepts are detected from the prompt, their cross-attention maps are aggregated, and concept-specific stamps are localized near the corresponding visual regions, illustrating a path from global disclosure to localized content/IP attribution without retraining the diffusion model.