On the Strengths of Pure Evolutionary Algorithms in Generating Adversarial Examples
Antony Bartlett, Cynthia C. S. Liem, Annibale Panichella · 2023
Deep learning (DL) models are known to be highly accurate, yet vulnerable to adversarial examples. While earlier research focused on generating adversarial examples using white-box strategies, later research focused on black-box strategies, as models often are not accessible to external attackers. Prior studies showed that black-box approaches based on approximate gradient descent algorithms combined with meta-heuristic search (i.e., the BMI-FGSM algorithm) outperform previously proposed white- and black-box strategies. In this paper, we propose a novel black-box approach purely based on differential evolution (DE), i.e., without using any gradient approximation method. In particular, we propose two variants of a customized DE with customized variation operators: (1) a single-objective (Pixel-SOO) variant generating attacks that fool DL models, and (2) a multi-objective variant (Pixel-MOO) that also minimizes the number of changes in generated attacks. Our preliminary study on five canonical image classification models shows that Pixel-SOO and Pixel-MOO are more effective than the state-of-the-art BMI-FGSM in generating adversarial attacks. Furthermore, Pixel-SOO is faster than Pixel-MOO, while the latter produces subtler attacks than its single-objective variant.