ABCAttack: A Gradient-Free Optimization Black-Box Attack for Fooling Deep Image Classifiers

Abstract

The vulnerability of deep neural network (DNN)-based systems makes them susceptible to adversarial perturbation and may cause classification task failure. In this work, we propose an adversarial attack model using the Artificial Bee Colony (ABC) algorithm to generate adversarial samples without the need for a further gradient evaluation and training of the substitute model, which can further improve the chance of task failure caused by adversarial perturbation. In untargeted attacks, the proposed method obtained 100%, 98.6%, and 90.00% success rates on the MNIST, CIFAR-10 and ImageNet datasets, respectively. The experimental results show that the proposed ABCAttack can not only obtain a high attack success rate with fewer queries in the black-box setting, but also break some existing defenses to a large extent, and is not limited by model structure or size, which provides further research directions for deep learning evasion attacks and defenses.

EFSAttack: Edge Noise-Constrained Black-Box Attack Using Artificial Fish Swarm Algorithm

“…This algorithm first divides the search space and initializes the particle swarm by randomly selecting a few image blocks to apply perturbations. It then performs an optimization search.• ABCAttack[28]. ABCAttack generates adversarial examples based on the artificial bee colony algorithm.…”