RWP: a robust watermarking plugin for attribution and protection in stable diffusion models.

Area of Science:

• Artificial Intelligence
• Computer Vision
• Information Security

Background:

• Diffusion models excel at content generation but pose provenance tracking challenges.
• Misuse of black-box models and deceptive content require robust solutions.
• Existing watermarking methods struggle with commercial AI scenarios and user tracing accuracy.

Purpose of the Study:

• Introduce RWP, a robust watermarking plugin for AI commercial applications.
• Address limitations in current watermarking techniques for diffusion models.
• Enhance traceability and accountability in AI-generated content.

Main Methods:

• Embed traceable watermarking information via a reversible encryption module within the diffusion model's generation process.
• Develop a plugin-based approach that requires no modifications to the diffusion model architecture.
• Incorporate unique transformation sequences for robust data embedding.

Main Results:

• Achieve 20-50% improvement in tracking accuracy against image-to-image translation and inpainting attacks compared to AquaLoRA.
• Maintain high visual fidelity of generated content.
• Expand traceable user capacity from 48-64 bits to over 400 bits for finer-grained identification.

Conclusions:

• RWP offers a practical and effective solution for copyright protection and accountability in large-scale commercial AI applications.
• The plugin architecture significantly improves deployment efficiency for watermarking systems.
• The enhanced user capacity supports robust tracing and deters model misuse.