EvoIBA: Evolutionary Boundary Attack Under Hard-Label Black Box Condition

Research has shown that deep neural networks (DNNs) have vulnerabilities that can lead to the misrecognition of Adversarial Examples (AEs) with specifically designed pertur-bations. Various adversarial attack methods have been proposed to detect vulnerabilities under hard-label black box (HL-BB) conditions in the absence of loss gradients and confidence scores. However, these methods fall into local solutions because they search only local regions of the search space. Therefore, this study proposes an adversarial attack method named EvolBA to generate AEs using Covariance Matrix Adaptation Evolution Strategy (CMA-ES) under the HL-BB condition, where only a class label predicted by the target DNN model is available. Inspired by formula-driven supervised learning, the proposed method introduces domain-independent operators for the initial-ization process and a jump that enhances search exploration. Experimental results confirmed that the proposed method could determine AEs with smaller perturbations than previous methods in images where the previous methods have difficulty.