Problem
Research questions and friction points this paper is trying to address.
Text-guided diffusion editing
disinformation
image provenance
watermark
white-box access
AngelFingerprint: A Traceable, Explainable, and White-Box Stealthy Watermark for Text-Guided Image Editing
🤖 AI Summary
为解决文本引导图像编辑中的误导信息问题，提出AngelFingerprint框架，通过将编辑提示嵌入模型权重中来实现可追溯、可解释且难以被检测的水印。
📝 Abstract
Text-guided diffusion editing raises disinformation concerns, making reliable image provenance essential. While watermarks are commonly used for this purpose, most methods carry a fixed ID that cannot explain what was changed and which prompt produced it. Furthermore, under open-source white-box access, attackers can easily locate and remove watermarks added as separate modules. Targeting this setting, we propose AngelFingerprint, a novel watermarking framework ensuring edit traceability, explainability, and white-box stealthiness. It integrates a LoRA into the diffusion model to embed the editing prompt's CLIP text embedding directly into the model's weights. An extractor then recovers this embedding from the image pixels alone. This semantic payload explains the edit, while the weight-integrated design makes it hard to detect and isolate even under full white-box access. Two techniques make this possible: a velocity-alignment anchor that preserves edit quality, and a specially designed frequency filter that keeps the watermark imperceptible yet recoverable and robust. On the MagicBrush dataset, our extractor achieves $86\%$ top-1 accuracy in a 200-way prompt retrieval, versus $20\%$ for prompt inversion.
Innovation
Methods, ideas, or system contributions that make the work stand out.
AngelFingerprint
LoRA
CLIP text embedding
white-box stealthiness
frequency filter
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