研究语音障碍检测系统漏洞，发现攻击可轻易误导诊断结果。
Vulnerabilities in Machine Learning-Based Voice Disorder Detection Systems
- 从原始音频出发，测试对抗、逃避和变调攻击
- 多类攻击使先进模型分类错误率显著上升
- 提醒医疗AI系统需加强安全防护，适合关注AI安全的研究者
语音障碍的健康影响日益受到重视。近年来，多项基于机器学习的分类器被用于区分正常与病理性语音。本文聚焦于分析此类系统的脆弱性，探索能否通过攻击手段反转分类结果并破坏其可靠性。鉴于个人健康信息的重要性，明确有效攻击方式是提升医疗领域机器学习系统安全性的关键第一步。我们从原始音频出发，实施多种攻击方法，包括对抗攻击、逃避攻击和变调技术，并评估当前最先进的语音障碍检测模型对这些攻击的响应。研究结果揭示了最有效的攻击策略，强调了在医疗领域应用的机器学习系统中必须应对这些安全隐患。
原文摘要 · Abstract (English)
The impact of voice disorders is becoming more widely acknowledged as a public health issue. Several machine learning-based classifiers with the potential to identify disorders have been used in recent studies to differentiate between normal and pathological voices and sounds. In this paper, we focus on analyzing the vulnerabilities of these systems by exploring the possibility of attacks that can reverse classification and compromise their reliability. Given the critical nature of personal health information, understanding which types of attacks are effective is a necessary first step toward improving the security of such systems. Starting from the original audios, we implement various attack methods, including adversarial, evasion, and pitching techniques, and evaluate how state-of-the-art disorder detection models respond to them. Our findings identify the most effective attack strategies, underscoring the need to address these vulnerabilities in machine-learning systems used in the healthcare domain.
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