A Comparative Analysis of Evolutionary Adversarial One-Pixel Attacks

Adversarial attacks pose significant challenges to the robustness of machine learning models. This paper explores the one-pixel attacks in image classification, a black-box adversarial attack that introduces changes to the pixels of the input images to make the classifier predict erroneously. We use a pragmatic approach by employing different evolutionary algorithms - Differential Evolution, Genetic Algorithms, and Covariance Matrix Adaptation Evolution Strategy - to find and optimise these one-pixel attacks. We focus on understanding how these algorithms generate effective one-pixel attacks. The experimentation was carried out on the CIFAR-10 dataset, a widespread benchmark in image classification. The experimental results cover an analysis of the following aspects: fitness optimisation, number of evaluations to generate an adversarial attack, success rate, number of adversarial attacks found per image, solution space coverage and level of distortion done to the original image to generate the attack. Overall, the experimentation provided insights into the nuances of the one-pixel attack and compared three standard evolutionary algorithms, showcasing each algorithm’s potential and evolutionary computation’s ability to find solutions in this strict case of the adversarial attack.