Biologically Inspired Adaptive-Q Filterbanks for Replay Spoofing Attack Detection

Wickramasinghe, Buddhi; Ambikairajah, Eliathamby; Epps, Julien

doi:10.21437/interspeech.2019-1535

Cited by 2 publications

(2 citation statements)

References 30 publications

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“…[72] proposes the use of decision level feature switching between mel and linear filterbank slope based features, demonstrating promising performance on the ASVspoof 2017 v2.0 dataset. Adaptive filterbank based features for spoofing detection have been proposed in [73]. Finally, [74] proposes the use of convolutional restricted Boltzmann machines (RBMs) to learn temporal modulation features for spoofing detection.…”

Section: Related Workmentioning

confidence: 99%

Deep generative variational autoencoding for replay spoof detection in automatic speaker verification

Chettri

Kinnunen

Benetos

2020

Computer Speech & Language

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Automatic speaker verification (ASV) systems are highly vulnerable to presentation attacks, also called spoofing attacks. Replay is among the simplest attacks to mount -yet difficult to detect reliably. The generalization failure of spoofing countermeasures (CMs) has driven the community to study various alternative deep learning CMs. The majority of them are supervised approaches that learn a human-spoof discriminator. In this paper, we advocate a different, deep generative approach that leverages from powerful unsupervised manifold learning in classification. The potential benefits include the possibility to sample new data, and to obtain insights to the latent features of genuine and spoofed speech. To this end, we propose to use variational autoencoders (VAEs) as an alternative backend for replay attack detection, via three alternative models that differ in their class-conditioning. The first one, similar to the use of Gaussian mixture models (GMMs) in spoof detection, is to train independently two VAEs -one for each class. The second one is to train a single conditional model (C-VAE) by injecting a one-hot class label vector to the encoder and decoder networks. Our final proposal integrates an auxiliary classifier to guide the learning of the latent space. Our experimental results using constant-Q cepstral coefficient (CQCC) features on the ASVspoof 2017 and 2019 physical access subtask datasets indicate that the C-VAE offers substantial improvement in comparison to training two separate VAEs for each class. On the 2019 dataset, the C-VAE outperforms the VAE and the baseline GMM by an absolute 9 -10% in both equal error rate (EER) and tandem detection cost function (t-DCF) metrics. Finally, we propose VAE residuals -the absolute difference of the original input and the reconstruction as features for spoofing detection. The proposed frontend approach augmented with a convolutional neural network classifier demonstrated substantial improvement over the VAE backend use case.

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Section: Related Workmentioning

confidence: 99%

Deep generative variational autoencoding for replay spoof detection in automatic speaker verification

Chettri

Kinnunen

Benetos

2020

Computer Speech & Language

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“…[10]), there are methods leveraging from modulation domain processing (e.g. [11,12,13]), adaptive filterbanks [14], restricted Boltzmann machines [15], and envelopes of subband signals [16] to name a few. In this work, we focus on modeling spectral subbands obtained through a standard windowed discrete Fourier transform.…”

Section: Relation To Prior Workmentioning

confidence: 99%

Ensemble Models for Spoofing Detection in Automatic Speaker Verification

et al. 2019

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Spectrograms -time-frequency representations of audio signals -have found widespread use in neural network-based spoofing detection. While deep models are trained on the fullband spectrum of the signal, we argue that not all frequency bands are useful for these tasks. In this paper, we systematically investigate the impact of different subbands and their importance on replay spoofing detection on two benchmark datasets: ASVspoof 2017 v2.0 and ASVspoof 2019 PA. We propose a joint subband modelling framework that employs n different sub-networks to learn subband specific features. These are later combined and passed to a classifier and the whole network weights are updated during training. Our findings on the ASVspoof 2017 dataset suggest that the most discriminative information appears to be in the first and the last 1 kHz frequency bands, and the joint model trained on these two subbands shows the best performance outperforming the baselines by a large margin. However, these findings do not generalise on the ASVspoof 2019 PA dataset. This suggests that the datasets available for training these models do not reflect real world replay conditions suggesting a need for careful design of datasets for training replay spoofing countermeasures.

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Biologically Inspired Adaptive-Q Filterbanks for Replay Spoofing Attack Detection

Cited by 2 publications

References 30 publications

Deep generative variational autoencoding for replay spoof detection in automatic speaker verification

Deep generative variational autoencoding for replay spoof detection in automatic speaker verification

Ensemble Models for Spoofing Detection in Automatic Speaker Verification

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