It is well-known that classifiers are vulnerable to adversarial perturbations. To defend against adversarial perturbations, various certified robustness results have been derived. However, existing certified robustnesses are limited to top-1 predictions. In many real-world applications, top-k predictions are more relevant. In this work, we aim to derive certified robustness for top-k predictions. In particular, our certified robustness is based on randomized smoothing, which turns any classifier to a new classifier via adding noise to an input example. We adopt randomized smoothing because it is scalable to large-scale neural networks and applicable to any classifier. We derive a tight robustness in 2 norm for topk predictions when using randomized smoothing with Gaussian noise. We find that generalizing the certified robustness from top-1 to top-k predictions faces significant technical challenges. We also empirically evaluate our method on CI-FAR10 and ImageNet. For example, our method can obtain an ImageNet classifier with a certified top-5 accuracy of 62.8% when the 2 -norms of the adversarial perturbations are less than 0.5 (=127/255). Our code is publicly available at: https
Jia et al. REF derived a tight certified robustness of top-k predictions for randomized smoothing with Gaussian noise.
Certified Robustness for Top-k Predictions against Adversarial Perturbations via Randomized Smoothing
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Mathematics Computer Science