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In this paper, we propose a new key-based defense focusing on both efficiencyand robustness. Although the previous key-based defense seems effective indefending against adversarial examples, carefully designed adaptive attacks canbypass the previous defense, and it is difficult to train the previous defenseon large datasets like ImageNet. We build upon the previous defense with twomajor improvements: (1) efficient training and (2) optional randomization. Theproposed defense utilizes one or more secret patch embeddings and classifierheads with a pre-trained isotropic network. When more than one secretembeddings are used, the proposed defense enables randomization on inference.Experiments were carried out on the ImageNet dataset, and the proposed defensewas evaluated against an arsenal of state-of-the-art attacks, includingadaptive ones. The results show that the proposed defense achieves a highrobust accuracy and a comparable clean accuracy compared to the previouskey-based defense.

Hindering Adversarial Attacks with Multiple Encrypted Patch Embeddings