Efficient, Robust, and Anti-Collusion Fingerprinting of Image Diffusion Models

2024-08-11 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Expert, extended

Summary

A novel framework addresses the critical vulnerability of image diffusion model fingerprinting to collusion attacks. The method embeds user-specific identifiers into a Personalized Normalization Module (PNM) within text-to-image (T2I) models, enabling reliable fingerprint recovery from generated images with over 99.5% accuracy. To counter collusion, an Anti-Collusion Transformation (ACT) is introduced, which applies lossless, function-invariant parameter changes (permutation, scaling, sign flip). This proactively degrades the image generation quality of colluded models, making them unusable; for instance, a 2-party collusion attack on Stable Diffusion v2 on the COCO dataset saw FID increase from 23 to 79. The framework also allows efficient, retraining-free creation of distinct model copies and incorporates a worst-case optimization strategy for robustness against model-level attacks like fine-tuning.

Key takeaway

For AI Security Engineers and ML Engineers concerned with intellectual property protection of generative models, this framework offers a robust defense against collusion attacks. Your teams should consider integrating personalized normalization modules with anti-collusion transformations to ensure that any unauthorized model averaging renders the colluded model unusable. This proactive approach prevents the functional redistribution of compromised models, significantly strengthening your IP defense strategy.

Key insights

Collusion-resistant fingerprinting for T2I models is achieved by embedding identifiers into a PNM and applying anti-collusion transformations.

Principles

• Fingerprints can be embedded via personalized normalization layers.
• Function-preserving parameter transformations disrupt model connectivity.
• Worst-case optimization enhances fingerprint robustness.

Method

Integrate a Personalized Normalization Module (PNM) into the VAE decoder, fine-tune with image reconstruction, fingerprinting, and worst-case regularization losses. Apply user-specific Anti-Collusion Transformations (ACT) (permutation, scaling, sign flip) to PNM parameters during model initialization.

In practice

• Distribute unique T2I model copies for IP protection.
• Identify unauthorized model redistribution or usage.
• Proactively disable colluded generative models.

Topics

• Image Diffusion Models
• Model Fingerprinting
• Collusion Attacks
• Intellectual Property Protection
• Personalized Normalization Module
• Anti-Collusion Transformation

Best for: Research Scientist, CTO, VP of Engineering/Data, AI Scientist, Machine Learning Engineer, AI Security Engineer

Related on AIssential

• Efficient, Robust, and Anti-Collusion Fingerprinting of Image Diffusion Models — A novel T2I model fingerprinting method uses a Personalized Normalization Module and anti-collusion transformations to ensure robustness against malicious model combinations.
• T2S: A Rehearsal-Based Approach for Extraction-Resistant Model Watermarking — T2S enhances AI model watermark robustness by simulating extraction attacks during embedding to boost transferability.
• Roots Beneath the Cut: Uncovering the Risk of Concept Revival in Pruning-Based Unlearning for Diffusion Models — Pruning-based unlearning in diffusion models is vulnerable to concept revival via side-channel information from pruned weight locations.
• From Vision to Text: A Compact Multimodal Approach for Robust, Cross-Domain Presentation Attack Detection on ID Cards — Compact multimodal PAD models need real-world data and fine-tuning for robust cross-domain performance, challenging synthetic dataset utility.
• Shafi Goldwasser Provides 'A Cryptographic Perspective on Trustworthy AI' — Cryptography offers a robust framework for defining, achieving, and proving trustworthiness in AI systems against adversarial threats.
• The Mirror Design Pattern: Strict Data Geometry over Model Scale for Prompt Injection Detection — Strict data geometry in prompt injection corpora enables highly effective, low-latency linear classifiers for L1 screening.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.