Detecting Replay Attacks Using Multi-Channel Audio: A Neural Network-Based Method
This addresses security vulnerabilities in voice-input systems, though it is incremental as it builds on prior single-channel methods.
The paper tackled the problem of replay attacks in voice-based security systems by introducing a neural network model that uses multi-channel audio to leverage spatial information, resulting in significantly improved detection performance.
With the rapidly growing number of security-sensitive systems that use voice as the primary input, it becomes increasingly important to address these systems' potential vulnerability to replay attacks. Previous efforts to address this concern have focused primarily on single-channel audio. In this paper, we introduce a novel neural network-based replay attack detection model that further leverages spatial information of multi-channel audio and is able to significantly improve the replay attack detection performance.