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In this paper, we propose a novel defensive transformation that enables us tomaintain a high classification accuracy under the use of both clean images andadversarial examples for adversarially robust defense. The proposedtransformation is a block-wise preprocessing technique with a secret key toinput images. We developed three algorithms to realize the proposedtransformation: Pixel Shuffling, Bit Flipping, and FFX Encryption. Experimentswere carried out on the CIFAR-10 and ImageNet datasets by using both black-boxand white-box attacks with various metrics including adaptive ones. The resultsshow that the proposed defense achieves high accuracy close to that of usingclean images even under adaptive attacks for the first time. In the best-casescenario, a model trained by using images transformed by FFX Encryption (blocksize of 4) yielded an accuracy of 92.30% on clean images and 91.48% under PGDattack with a noise distance of 8/255, which is close to the non-robustaccuracy (95.45%) for the CIFAR-10 dataset, and it yielded an accuracy of72.18% on clean images and 71.43% under the same attack, which is also close tothe standard accuracy (73.70%) for the ImageNet dataset. Overall, all threeproposed algorithms are demonstrated to outperform state-of-the-art defensesincluding adversarial training whether or not a model is under attack.

Block-wise Image Transformation with Secret Key for Adversarially Robust Defense