Audio Replay Attack Detection with Deep Learning Frameworks

By G. Lavrentyeva, Sergey Novoselov, Egor Malykh, Alexander Kozlov, Oleg Kudashev, Vadim Shchemelinin. Interspeech, 2017

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How researchers see this paper

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STC Antispoofing Systems for the ASVspoof2019 Challenge (2019, methodology): “All of the proposed systems for both scenarios were based on the enhanced Light CNN architecture previously used for replay attack detection [3].”
AASIST: Audio Anti-Spoofing Using Integrated Spectro-Temporal Graph Attention Networks (2021, methodology): “The new “ max graph operation ” (MGO), highlighted with a large grey box in Figure 1, is inspired by a number of works in the anti-spooﬁng literature which showed the beneﬁt of element-wise maximum operations [11, 26].”
ReMASC: Realistic Replay Attack Corpus for Voice Controlled Systems (2019, methodology): “Many prior efforts (such as presented in [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21]) have attempted to differentiate between original and replayed speech using the RedDots Replayed data set [22].”
Does Audio Deepfake Detection Generalize? (2022, unknown): “Another common architecture for audio spoof detection are LCNN-based learning models such as LCNN , LCNN-Attention , and LCNN-LSTM [12, 13, 14].”