BounceAttack: A Query-Efficient Decision-based Adversarial Attack by Bouncing into the Wild

Deep neural networks are vulnerable to adversarial attacks. We study such threats in the decision-based black-box setting where the adversary could obtain only the predicted labels of the victim classifier within limited queries and aims at performing targeted and untargeted adversarial attacks under different perturbation constraints. In this paper, we propose BounceAttack as a query-efficient attack method. We propose the bounce vector which encourages the iterations to maximally explore the adversarial space towards the optimal adversarial example within limited queries to improve the query efficiency. We perform extensive experiments on various benchmark datasets and models. Experimental results show that BounceAttack achieves both high query efficiency and small perturbation size. BounceAttack outperforms existing attack methods. For example, BounceAttack achieves 48.1% smaller perturbation compared to the state-of-the-art attack methods on average using the same number of model queries.