Geometry-Inspired Top-k Adversarial Perturbations

TL;DR

This paper introduces a geometry-inspired method for generating Top-k adversarial perturbations that effectively remove the true class from Top-k predictions, outperforming existing techniques and enabling universal attacks.

Contribution

It presents a novel fast multi-objective optimization approach for Top-k adversarial examples and introduces universal perturbations that are image-agnostic and highly effective.

Findings

Our method outperforms baseline adversarial techniques.

Universal perturbations significantly reduce true class presence in Top-k.

Approach improves existing universal adversarial perturbation methods.

Abstract

The brittleness of deep image classifiers to small adversarial input perturbations has been extensively studied in the last several years. However, the main objective of existing perturbations is primarily limited to change the correctly predicted Top-1 class by an incorrect one, which does not intend to change the Top-k prediction. In many digital real-world scenarios Top-k prediction is more relevant. In this work, we propose a fast and accurate method of computing Top-k adversarial examples as a simple multi-objective optimization. We demonstrate its efficacy and performance by comparing it to other adversarial example crafting techniques. Moreover, based on this method, we propose Top-k Universal Adversarial Perturbations, image-agnostic tiny perturbations that cause the true class to be absent among the Top-k prediction for the majority of natural images. We experimentally show that our approach outperforms baseline methods and even improves existing techniques of finding Universal Adversarial Perturbations.