Capsule Network based End-to-end System for Detection of Replay Attacks

Automatic speaker verification systems are prone to various spoofing attacks. The convolutional neural networks are found to be effective for detection of spoofing attacks. However, they lack spatial information and relationship of low-level features with the pooling layer. On the other hand, capsule networks use vectors to record spatial information and the probability of presence simultaneously. They are known to be effective for detection of forged images and videos. In this work, we study capsule networks for replay attack detection. We consider different input features to capsule network and study on recent ASVspoof 2019 physical access corpus. The studies suggest the proposed capsule network based system performs effectively and the performance is comparable to state-of-the-art single systems for replay attack detection.