  Research has shown that deep neural networks (DNNs) have vulnerabilities that
can lead to the misrecognition of Adversarial Examples (AEs) with specifically
designed perturbations. 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 initialization
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.
