FlowMur: A Stealthy and Practical Audio Backdoor Attack with Limited Knowledge
Jiahe Lan, Jie Wang, Baochen Yan, Zheng Yan, Elisa Bertino
IEEE Symposium on Security and Privacy 2024 · Day 2 · Continental Ballroom 5
This presentation introduces FlowMur, a novel and highly effective audio backdoor attack designed to operate with limited knowledge of the target system. Developed through a collaboration between CID University in China and KU University in the US, FlowMur addresses significant limitations present in existing audio backdoor methodologies. The research highlights critical vulnerabilities within speech recognition systems (SRS), which are increasingly integrated into daily life and safety-critical applications, ranging from transcription services and smart devices to in-car systems.
AI review
FlowMur presents a critical advancement in audio backdoor attacks, achieving superior stealth and effectiveness under a highly realistic limited-knowledge adversary model. Its ability to bypass both traditional and advanced defenses demands immediate attention from anyone securing speech recognition systems. This is not theoretical; it's a blueprint for a potent real-world threat.