Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·Jun 12·5 min read
Efficient, Robust, and Anti-Collusion Fingerprinting of Image Diffusion Models
TL;DR
Existing fingerprinting methods for text-to-image (T2I) models are vulnerable to collusion attacks, where attackers combine models to obscure fingerprints. A new method was developed that embeds user-specific identifiers into a personalized normalization module (PNM) to enhance robustness against such attacks.
✦ Why It Matters
Engineers can implement this robust fingerprinting method to protect their T2I models from unauthorized use and redistribution.
Key Takeaways
- Implement personalized normalization modules for T2I models.
- Use lossless transformations to enhance fingerprint robustness.
- Create multiple model copies efficiently without retraining.
- Monitor FID scores to assess model vulnerability to collusion.
Full Summary
Generative text-to-image (T2I) models often use fingerprinting to protect intellectual property by embedding unique identifiers in generated images. However, current methods are susceptible to collusion attacks, where multiple attackers can combine their models to remove these identifiers.
To counter this, a novel fingerprinting method was introduced that integrates user-specific fingerprints into a personalized normalization module (PNM). This method employs lossless function-invariant parameter transformations to create an anti-collusion mechanism, which degrades the image quality of colluded models.
Experimental results showed that the new approach maintained high fidelity and robustness across various T2I tasks, with fingerprint extraction accuracy exceeding 99.5%. Additionally, it allows for efficient creation of multiple fingerprinted model copies without retraining.
The findings indicate a significant improvement in proactive robustness against collusion attacks, as evidenced by increased Fréchet Inception Distance (FID) scores for colluded models.