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- W3200547907 abstract "Adversarial attack is aimed at fooling the target classifier with imperceptible perturbation. Adversarial examples, which are carefully crafted with a malicious purpose, can lead to erroneous predictions, resulting in catastrophic accidents. To mitigate the effects of adversarial attacks, we propose a novel purification model called CAP-GAN. CAP-GAN takes account of the idea of pixel-level and feature-level consistency to achieve reasonable purification under cycle-consistent learning. Specifically, we utilize the guided attention module and knowledge distillation to convey meaningful information to the purification model. Once a model is fully trained, inputs would be projected into the purification model and transformed into clean-like images. We vary the capacity of the adversary to argue the robustness against various types of attack strategies. On the CIFAR-10 dataset, CAP-GAN outperforms other pre-processing based defenses under both black-box and white-box settings." @default.
- W3200547907 created "2021-09-27" @default.
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- W3200547907 date "2021-07-18" @default.
- W3200547907 modified "2023-09-25" @default.
- W3200547907 title "CAP-GAN: Towards Adversarial Robustness with Cycle-consistent Attentional Purification" @default.
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- W3200547907 doi "https://doi.org/10.1109/ijcnn52387.2021.9533322" @default.
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