Generating Transferable Adversarial Examples for Speech Classification

초록

Despite the success of deep neural networks, the existence of adversarial attacks has revealed the vul-nerability of neural networks in terms of security. Adversarial attacks add subtle noise to the original example, resulting in a false prediction. Although adversarial attacks have been mainly studied in the im-age domain, a recent line of research has discovered that speech classification systems are also exposed to adversarial attacks. By adding inaudible noise, an adversary can deceive speech classification systems and cause fatal issues in various applications, such as speaker identification and command recognition tasks. However, research on the transferability of audio adversarial examples is still limited. Thus, in this study, we first investigate the transferability of audio adversarial examples with different structures and conditions. Through extensive experiments, we discover that the transferability of audio adversarial ex-amples is related to their noise sensitivity. Based on the analyses, we present a new adversarial attack called noise injected attack that generates highly transferable audio adversarial examples by injecting ad-ditive noise during the gradient ascent process. Our experimental results demonstrate that the proposed method outperforms other adversarial attacks in terms of transferability.(c) 2023 Elsevier Ltd. All rights reserved.

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