Sok: Comprehensive Security Overview, Challenges, and Future Directions of Voice-Controlled Systems

The integration of Voice Control Systems (VCS) into smart devices and their growing presence in daily life accentuate the importance of their security. Current research has uncovered numerous vulnerabilities in VCS, presenting significant risks to user privacy and security. However, a cohesive and systematic examination of these vulnerabilities and the corresponding solutions is still absent. This lack of comprehensive analysis presents a challenge for VCS designers in fully understanding and mitigating the security issues within these systems. Addressing this gap, our study introduces a hierarchical model structure for VCS, providing a novel lens for categorizing and analyzing existing literature in a systematic manner. We classify attacks based on their technical principles and thoroughly evaluate various attributes, such as their methods, targets, vectors, and behaviors. Furthermore, we consolidate and assess the defense mechanisms proposed in current research, offering actionable recommendations for enhancing VCS security. Our work makes a significant contribution by simplifying the complexity inherent in VCS security, aiding designers in effectively identifying and countering potential threats, and setting a foundation for future advancements in VCS security research.

CLAD: Robust Audio Deepfake Detection Against Manipulation Attacks with Contrastive Learning

The increasing prevalence of audio deepfakes poses significant security threats, necessitating robust detection methods. While existing detection systems exhibit promise, their robustness against malicious audio manipulations remains underexplored. To bridge the gap, we undertake the first comprehensive study of the susceptibility of the most widely adopted audio deepfake detectors to manipulation attacks. Surprisingly, even manipulations like volume control can significantly bypass detection without affecting human perception. To address this, we propose CLAD (Contrastive Learning-based Audio deepfake Detector) to enhance the robustness against manipulation attacks. The key idea is to incorporate contrastive learning to minimize the variations introduced by manipulations, therefore enhancing detection robustness. Additionally, we incorporate a length loss, aiming to improve the detection accuracy by clustering real audios more closely in the feature space. We comprehensively evaluated the most widely adopted audio deepfake detection models and our proposed CLAD against various manipulation attacks. The detection models exhibited vulnerabilities, with FAR rising to 36.69%, 31.23%, and 51.28% under volume control, fading, and noise injection, respectively. CLAD enhanced robustness, reducing the FAR to 0.81% under noise injection and consistently maintaining an FAR below 1.63% across all tests. Our source code and documentation are available in the artifact repository (https://github.com/CLAD23/CLAD).