Abstract: Recent studies show that deep neural networks are vulnerable to adversarial attacks in the form of subtle perturbations to the input image, which leads the model to output wrong prediction.

Abstract: In the hard-label black-box setting, existing attack methods randomly select words for perturbation, generating invalid word replacement operations, resulting in low attack success rate.

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