DNN controlled adaptive front-end for replay attack detection systems

Developing robust countermeasures to protect automatic speaker verification systems against replay spoofing attacks is a well-recognized challenge. Current approaches to spoofing detection are generally based on a fixed front-end, typically a time-invariant filter bank, followed by a machine learning back-end. In this paper, we propose a novel approach whereby the front-end comprises an adaptive filter bank with a deep neural networkbased controller, which is jointly trained along with a neural network back-end. Specifically, the deep neural network-based adaptive filter controller tunes the selectivity and sensitivity of the front-end filter bank at every frame to capture replay-related artefacts. We demonstrate the effectiveness of the proposed framework in spoofing attack detection on a synthesized dataset and ASVSpoof 2019 and ASVSpoof 2021 challenge datasets in terms of equal error rate and its ability to capture artefacts that differentiate replayed signals from genuine ones in comparison to conventional non-adaptive front-ends.

Automated summary

This paper proposes a novel approach to protect automatic speaker verification systems against replay spoofing attacks. By using an adaptive filter bank and a deep neural network-based controller as the front-end, and jointly training with a neural network back-end, the effectiveness of the proposed framework is demonstrated in spoofing attack detection.