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- W3213187311 abstract "Generative adversarial networks (GANs) have made unprecedented performance in image synthesis and play a key role in various downstream applications of computer vision. However, GAN models trained on sensitive data also pose a distinct threat to privacy. In this poster, we present a novel over-representation based membership inference attack. Unlike prior attacks against GANs which focus on the overall metrics, such as the attack accuracy, our attack aims to make inference from the high-precision perspective, which allows the adversary to concentrate on inferring a sample as a member confidently. Initial experimental results demonstrate that the adversary can achieve a high precision attack even if the overall attack accuracy is about 50% for a well-trained GAN model. Our work will raise awareness of the importance of precision when GAN owners evaluate the privacy risks of their models." @default.
- W3213187311 created "2021-11-22" @default.
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- W3213187311 date "2021-11-12" @default.
- W3213187311 modified "2023-09-30" @default.
- W3213187311 title "Membership Inference Attacks against GANs by Leveraging Over-representation Regions" @default.
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- W3213187311 doi "https://doi.org/10.1145/3460120.3485338" @default.
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