论文标题
插件攻击：朝着强大而灵活的模型反转攻击
Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks
论文作者
论文摘要
模型反转攻击（MIAS）旨在创建合成图像，通过利用模型的学习知识来反映目标分类器的私人培训数据的范围特征。先前的研究开发了生成的MIA，该MIA使用使用生成的对抗网络（GAN）作为针对特定目标模型的图像先验。这使得攻击时间和资源消耗，不灵活，并且容易受到数据集之间的分配变化的影响。为了克服这些缺点，我们提出了插头攻击，从而放宽了目标模型和图像之前的依赖性，并启用单个gan来攻击广泛的目标，只需要对攻击进行少量调整。此外，我们表明，即使在公开获得的预训练的gan和强烈的分配变化下，也可以实现强大的MIA，而先前的方法无法产生有意义的结果。我们的广泛评估证实了插头攻击的鲁棒性和灵活性，以及它们创建高质量图像的能力，揭示了敏感的班级特征。
Model inversion attacks (MIAs) aim to create synthetic images that reflect the class-wise characteristics from a target classifier's private training data by exploiting the model's learned knowledge. Previous research has developed generative MIAs that use generative adversarial networks (GANs) as image priors 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, which relax the dependency between the target model and image prior, and enable the use of a single GAN to attack a wide range of targets, requiring only minor adjustments to the attack. Moreover, we show that powerful MIAs are possible even with publicly available pre-trained GANs and under strong distributional shifts, for which 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.
