In light of the widespread application of Automatic Speech Recognition (ASR)
systems, their security concerns have received much more attention than ever
before, primarily due to the susceptibility of Deep Neural Networks. Previous
studies have illustrated that surreptitiously crafting adversarial
perturbations enables the manipulation of speech recognition systems, resulting
in the production of malicious commands. These attack methods mostly require
adding noise perturbations under $\ell_p$ norm constraints, inevitably leaving
behind artifacts of manual modifications. Recent research has alleviated this
limitation by manipulating style vectors to synthesize adversarial examples
based on Text-to-Speech (TTS) synthesis audio. However, style modifications
based on optimization objectives significantly reduce the controllability and
editability of audio styles. In this paper, we propose an attack on ASR systems
based on user-customized style transfer. We first test the effect of Style
Transfer Attack (STA) which combines style transfer and adversarial attack in
sequential order. And then, as an improvement, we propose an iterative Style
Code Attack (SCA) to maintain audio quality. Experimental results show that our
method can meet the need for user-customized styles and achieve a success rate
of 82% in attacks, while keeping sound naturalness due to our user study.

在自动语音识别系统广泛应用的背景下，我们提出了一种基于用户自定义风格转换的攻击方法，结合风格转换和对抗攻击的顺序来实现对 ASR 系统的攻击，实验结果表明我们的方法能够满足用户个性化风格的需求，并在攻击中达到了 82% 的成功率，并且由于我们的用户研究保持了音频的自然性。