Enhancing Membership Inference Attacks on Diffusion Models from a Frequency-Domain Perspective
arXiv:2505.20955v4 Announce Type: replace-cross Abstract: Diffusion models have achieved tremendous success in image generation, but they also raise significant concerns regarding privacy and copyright issues. Membership Inference Attacks (MIAs) are designed to ascertain whether specific data was utilized during a model's training phase. As current MIAs for diffusion models typically exploit the model's image prediction ability, we formalize them into a unified general paradigm that computes the membership score for membership identification. Under this paradigm, we empirically find that exist
The rapid advancement and widespread deployment of diffusion models necessitate immediate scrutiny of their security and privacy vulnerabilities, especially as they become integral to various applications.
Enhanced membership inference attacks pose a significant threat to the privacy of training data for AI models, potentially undermining trust and legal compliance in generative AI systems.
The improved understanding and formalization of membership inference attacks mean that current mitigation strategies for diffusion models may be insufficient and require re-evaluation.
- · Cybersecurity researchers
- · Privacy-enhancing technology developers
- · Regulatory bodies
- · Developers of diffusion models
- · Users of sensitive data in AI training
- · Companies relying on opaque AI training data
More robust privacy-preserving techniques will be developed and integrated into diffusion model training pipelines.
Increased legal and ethical pressure on AI developers to demonstrate proof of data provenance and privacy guarantees.
The development of a new sub-field dedicated to 'adversarial privacy' in generative AI, mirroring adversarial robust AI.
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Read at arXiv cs.LG