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- W3091761040 abstract "Conventional adversarial defenses reduce classification accuracy whether or not a model is under attacks. Moreover, most of image processing based defenses are defeated due to the problem of obfuscated gradients. In this paper, we propose a new adversarial defense which is a defensive transform for both training and test images inspired by perceptual image encryption methods. The proposed method utilizes a block-wise pixel shuffling method with a secret key. The experiments are carried out on both adaptive and non-adaptive maximum-norm bounded white-box attacks while considering obfuscated gradients. The results show that the proposed defense achieves high accuracy (91.55%) on clean images and (89.66%) on adversarial examples with noise distance of 8/255 on CFAR-10 dataset. Thus, the proposed defense outperforms state-of-the-art adversarial defenses including latent adversarial training, adversarial training and thermometer encoding." @default.
- W3091761040 created "2020-10-08" @default.
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- W3091761040 date "2020-10-01" @default.
- W3091761040 modified "2023-09-29" @default.
- W3091761040 title "Encryption Inspired Adversarial Defense For Visual Classification" @default.
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- W3091761040 doi "https://doi.org/10.1109/icip40778.2020.9190904" @default.
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