通过欺骗性输入探索多语言语音翻译系统中的安全漏洞.
IEEE transactions on pattern analysis and machine intelligence
|January 28, 2026
概括
研究人员利用不可察觉的音频操纵探索了语音翻译 (ST) 系统中的漏洞. 攻击,包括适应的自动语音识别 (ASR) 技术和创新音乐生成,成功破坏了ST模型,揭示了系统弱点.
科学领域:
- 自然语言处理自然语言处理.
- 语音技术 语言技术
- 网络安全 网络安全
背景情况:
- 语音翻译 (ST) 系统越来越普遍,但其安全漏洞尚未得到充分了解.
- 稳健可靠的通信依赖于安全的ST系统.
- 有限的研究存在于通过音频操纵损害ST系统.
研究的目的:
- 调查使用不可察觉的音频操纵来破坏语音翻译系统的方法.
- 为ST适应现有的自动语音识别 (ASR) 攻击技术.
- 开发新的方法,如音乐生成,用于秘密的ST攻击.
主要方法:
- 调整基于扰乱的技术,从自动语音识别 (ASR) 攻击到语音翻译 (ST).
- 开发一种新的音乐生成方法,以指导有针对性的翻译.
- 在现实世界的场景中进行空袭.
主要成果:
- 精心设计的音频干扰可以导致ST模型产生有针对性的有害输出.
- 敌对的音乐可以通过利用自然的不可感知性来秘密地破坏ST系统.
- 攻击在多种语言和ST模型中表现出有效性,表明系统漏洞.
结论:
- 目前的语音翻译架构对音频操纵攻击具有系统性漏洞.
- 这些发现突出了神经语音系统的稳定性和可解释性的更广泛挑战.
- 不易察觉的音频操纵对ST系统的安全性和可靠性构成重大威胁.
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