Uncovers threats to automatic speech recognition systems in hard-label black-box environments, suggesting a new attack method.
Adversarial examples are inputs with slight intentional perturbations that lead to incorrect predictions of deep neural networks; they also threaten automatic speech recognition systems. This study proposes a method to uncover such threats in practical environments, focusing on hard‐label black‐box settings.
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Akagaki et al. (2026) studied this question.
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