Deep models are state-of-the-art for many computer vision tasks including image classification and object detection. However, it has been shown that deep models are vulnerable to adversarial examples. We highlight how one-hot encoding directly contributes to this vulnerability and propose breaking away from this widely-used, but highly-vulnerable mapping. We demonstrate that by leveraging a different output encoding, multi-way encoding, we can make models more robust. Our approach makes it more difficult for adversaries to find useful gradients for generating adversarial attacks. We present state-of-the-art robustness results for black-box, white-box attacks, and achieve higher clean accuracy on four benchmark datasets: MNIST, CIFAR-10, CIFAR-100, and SVHN when combined with adversarial training. The strength of our approach is also presented in the form of an attack for model watermarking, raising challenges in detecting stolen models.
We demonstrate that by leveraging a multi-way output encoding, rather than the widely used one-hot encoding, we can make deep models more robust to adversarial attacks. This paper proposes replacing the final cross-entropy layer trained on one-hot labels in classifiers by encoding each label as a high-dimensional vector and training the classifier to minimize L2 distance from the encoding of the correct class. Authors propose new method against adversarial attacks that shows significant amount of gains compared to baselines