Improving replay attack detection by combination of spatial and spectral features

Yaguchi, Ryoya; Shiota, Sayaka; Kiya, Hitoshi

doi:10.1109/apsipaasc47483.2019.9023181

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Cited by 4 publications

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“…The limitation is that the pop noise effect disappears over larger distances and thus the method is only applicable to close-field situations. In [30], [31], the authors use generalized cross-correlation (GCC) of the non-speech sections of a stereo signal to detect the replay attack. The idea is that loudspeakers tend to generate electromagnetic noise during the non-speech section, and therefore the cross-correlation between the twochannel signals will be higher for replayed samples than genuine samples in the non-speech section.…”

Section: Introductionmentioning

confidence: 99%

Detecting Replay Attacks Using Multi-Channel Audio: A Neural Network-Based Method

Gong,

Yang,

Poellabauer

2020

Preprint

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With the rapidly growing number of securitysensitive 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.

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Section: Introductionmentioning

confidence: 99%