Vulnerabilities in Machine Learning-Based Voice Disorder Detection Systems
This work addresses security risks in healthcare AI systems, which is critical for protecting personal health information, though it is incremental as it focuses on identifying vulnerabilities rather than proposing new defenses.
The paper analyzed vulnerabilities in machine learning-based voice disorder detection systems by implementing adversarial, evasion, and pitching attacks, finding that certain strategies are highly effective at compromising model reliability.
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.