Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks

• URL: http://arxiv.org/abs/2201.12179v1
• Date: Fri, 28 Jan 2022 15:25:50 GMT
• Title: Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks
• Authors: Lukas Struppek, Dominik Hintersdorf, Antonio De Almeida Correia, Antonia Adler, Kristian Kersting
• Abstract summary: Model attacks (MIAs) aim to create synthetic images that reflect the class-wise characteristics from a target inversion's training data by exploiting the model's learned knowledge. Previous research has developed generative MIAs using generative adversarial networks (GANs) as image priors tailored to a specific target model. We present Plug & Play Attacks that loosen the dependency between the target model and image prior and enable the use of a single trained GAN to attack a broad range of targets.
• Score: 13.374754708543449
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Model inversion attacks (MIAs) aim to create synthetic images that reflect the class-wise characteristics from a target classifier's training data by exploiting the model's learned knowledge. Previous research has developed generative MIAs using generative adversarial networks (GANs) as image priors that are tailored to a specific target model. This makes the attacks time- and resource-consuming, inflexible, and susceptible to distributional shifts between datasets. To overcome these drawbacks, we present Plug & Play Attacks that loosen the dependency between the target model and image prior and enable the use of a single trained GAN to attack a broad range of targets with only minor attack adjustments needed. Moreover, we show that powerful MIAs are possible even with publicly available pre-trained GANs and under strong distributional shifts, whereas previous approaches fail to produce meaningful results. Our extensive evaluation confirms the improved robustness and flexibility of Plug & Play Attacks and their ability to create high-quality images revealing sensitive class characteristics.

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