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Black-box Membership Inference Attacks against Fine-tuned Diffusion Models
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abstract
With the rapid advancement of diffusion-based image-generative models, the quality of generated images has become increasingly photorealistic. Moreover, with the release of high-quality pre-trained image-generative models, a growing number of users are downloading these pre-trained models to fine-tune them with downstream datasets for various image-generation tasks. However, employing such powerful pre-trained models in downstream tasks presents significant privacy leakage risks. In this paper, we propose the first reconstruction-based membership inference attack framework, tailored for recent diffusion models, and in the more stringent black-box access setting. Considering four distinct attack scenarios and three types of attacks, this framework is capable of targeting any popular conditional generator model, achieving high precision, evidenced by an impressive AUC of $0.95$.
Forward citations
Cited by 3 Pith papers
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SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation
A systematic survey and benchmark showing that diffusion-based synthetic data can achieve better utility-privacy tradeoffs than DP-SGD on real data for some image classifiers, with the best release strategy depending ...
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Enhancing Membership Inference Attacks on Diffusion Models from a Frequency-Domain Perspective
Removing high-frequency components from reconstruction-error scores improves membership inference attacks on diffusion models, demonstrated on DDIM and Stable Diffusion.
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Membership Inference Attacks on Tokenizers of Large Language Models
Tokenizers leak dataset membership: using distinctive tokens that appear in a dataset's text, an attacker can detect with AUC up to 0.77 whether that dataset was part of tokenizer training.
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