Unstoppable Attack: Label-Only Model Inversion via Conditional Diffusion Model

• URL: http://arxiv.org/abs/2307.08424v3
• Date: Wed, 6 Mar 2024 05:00:06 GMT
• Title: Unstoppable Attack: Label-Only Model Inversion via Conditional Diffusion Model
• Authors: Rongke Liu, Dong Wang, Yizhi Ren, Zhen Wang, Kaitian Guo, Qianqian Qin, Xiaolei Liu
• Abstract summary: Model attacks (MIAs) aim to recover private data from inaccessible training sets of deep learning models. This paper develops a novel MIA method, leveraging a conditional diffusion model (CDM) to recover representative samples under the target label. Experimental results show that this method can generate similar and accurate samples to the target label, outperforming generators of previous approaches.
• Score: 14.834360664780709
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Model inversion attacks (MIAs) aim to recover private data from inaccessible training sets of deep learning models, posing a privacy threat. MIAs primarily focus on the white-box scenario where attackers have full access to the model's structure and parameters. However, practical applications are usually in black-box scenarios or label-only scenarios, i.e., the attackers can only obtain the output confidence vectors or labels by accessing the model. Therefore, the attack models in existing MIAs are difficult to effectively train with the knowledge of the target model, resulting in sub-optimal attacks. To the best of our knowledge, we pioneer the research of a powerful and practical attack model in the label-only scenario. In this paper, we develop a novel MIA method, leveraging a conditional diffusion model (CDM) to recover representative samples under the target label from the training set. Two techniques are introduced: selecting an auxiliary dataset relevant to the target model task and using predicted labels as conditions to guide training CDM; and inputting target label, pre-defined guidance strength, and random noise into the trained attack model to generate and correct multiple results for final selection. This method is evaluated using Learned Perceptual Image Patch Similarity as a new metric and as a judgment basis for deciding the values of hyper-parameters. Experimental results show that this method can generate similar and accurate samples to the target label, outperforming generators of previous approaches.

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