Geometry-Inspired Top-k Adversarial Perturbations

State-of-the-art deep learning models are untrustworthy due to their vulnerability to adversarial examples. Intriguingly, besides simple adversarial perturbations, there exist Universal Adversarial Perturbations (UAPs), which are input-agnostic perturbations that lead to misclassification of majority inputs. The main target of existing adversarial examples (including UAPs) is to change primarily the correct Top-1 predicted class by the incorrect one, which does not guarantee changing the Top-k prediction. However, in many real-world scenarios, dealing with digital data, Top-k predictions are more important. We propose an effective geometry-inspired method of computing Top-k adversarial examples for any k. We evaluate its effectiveness and efficiency by comparing it with other adversarial example crafting techniques. Based on this method, we propose Top-k Universal Adversarial Perturbations, image-agnostic tiny perturbations that cause true class to be absent among the Top-k pre-diction. We experimentally show that our approach outperforms baseline methods and even improves existing techniques of generating UAPs.