Siamese Convolutional Neural Network Using Gaussian Probability Feature for Spoofing Speech Detection

Lei, Zhenchun; Yang, Yukun; Liu, Changhong; Ye, Jihua

doi:10.21437/interspeech.2020-2723

Cited by 23 publications

(9 citation statements)

References 15 publications

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“…Fig. 5 shows [13] 0.1000 5.06 FFT-LCNN [13] 0.1028 4.53 LFCC-Siamese CNN [15] 0.0930 3.79 FFT-LCGRNN [7] 0.0776 3.03 RW-Resnet [19] 0.0820 2.98 Ling et al [16] 0.0510 1.87 FFT-L-SENet [38] 0.0368 1.14 AASIST [7] 0.0347 1.13 LPS(F0) (ours) 0.0358 1.21 (b) Primary systems System t-DCF EER% T05 [28] 0.0069 0.22 T45 [13] 0.0510 1.84 T60 [3] 0.0755 2.64 GMM fusion [26] 0.0740 2.92 T24 [28] 0.0953 3.45 T50 [36] 0.1671 3.56 (Imag(L)+Real(H)) + LPS(F0) (ours) 0.0143 0.43 the detailed performance of LPS in different attacks of the evaluation set.…”

Section: Effectiveness Of F0 Subbandmentioning

confidence: 99%

Audio Deepfake Detection Based on a Combination of F0 Information and Real Plus Imaginary Spectrogram Features

Xue¹,

Fan²,

Zhao³

et al. 2022

Preprint

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Recently, pioneer research works have proposed a large number of acoustic features (log power spectrogram, linear frequency cepstral coefficients, constant Q cepstral coefficients, etc.) for audio deepfake detection, obtaining good performance, and showing that different subbands have different contributions to audio deepfake detection. However, this lacks an explanation of the specific information in the subband, and these features also lose information such as phase. Inspired by the mechanism of synthetic speech, the fundamental frequency (F0) information is used to improve the quality of synthetic speech, while the F0 of synthetic speech is still too average, which differs significantly from that of real speech. It is expected that F0 can be used as important information to discriminate between bonafide and fake speech, while this information cannot be used directly due to the irregular distribution of F0. Insteadly, the frequency band containing most of F0 is selected as the input feature. Meanwhile, to make full use of the phase and full-band information, we also propose to use real and imaginary spectrogram features as complementary input features and model

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Section: Effectiveness Of F0 Subbandmentioning

confidence: 99%

Audio Deepfake Detection Based on a Combination of F0 Information and Real Plus Imaginary Spectrogram Features

Xue¹,

Fan²,

Zhao³

et al. 2022

Preprint

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“…min-tDCF EER Spec+LFCC+CQT+SE-Res2Net [18] 0.0452 1.89 LFCC-GMM+GAT-S+GAT-T+RawNet2 0.0476 1.68 LFCC+LFCC-CMVN+CQT+FFT+LCNN+ 0.0510 1.86 LFCC-GMM [9] ResNet18+LMCL+FM [17] 0.0520 1.81 GAT-S+GAT-T+RawNet2 0.0635 2.21 LFCC-GMM+RawNet2 0.0643 2.33 GAT-S+RawNet2 0.0692 2.29 Ensemble model [10] 0.0755 2.64 GAT-S+GAT-T 0.0844 4.30 GAT-T+RawNet2 0.0854 2.61 GAT-T 0.0894 4.71 LFCC-GMM [7] 0.0904 3.50 GAT-S 0.0914 4.48 Siamese CNN [11] 0.0930 3.79 FG-CQT+LCNN+CE [41] 0.1020 4.07 LFB-ResNet18 [17] 0 ture and origins of the artefacts being detected with spectral and temporal attention and then to link these to specific spoofing attacks and the algorithmic origins.…”

Section: Systemmentioning

confidence: 99%

“…CNNs are particularly appealing because of their capacity to extract localised artefacts within spectro-temporal decompositions such as a spectrogram. For both the ASVspoof 2017 [8] and ASVspoof 2019 [9][10][11] challenges, CNN-based approaches were among the best performing systems. More elaborate systems, such as those based upon ResNet architectures, are now attracting greater interest, and enable the learning of deeper networks using residual blocks with skip connections [12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Graph Attention Networks for Anti-Spoofing

Tak¹,

Jung²,

Patino³

et al. 2021

Interspeech 2021

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The cues needed to detect spoofing attacks against automatic speaker verification are often located in specific spectral subbands or temporal segments. Previous works show the potential to learn these using either spectral or temporal selfattention mechanisms but not the relationships between neighbouring sub-bands or segments. This paper reports our use of graph attention networks (GATs) to model these relationships and to improve spoofing detection performance. GATs leverage a self-attention mechanism over graph structured data to model the data manifold and the relationships between nodes. Our graph is constructed from representations produced by a ResNet. Nodes in the graph represent information either in specific sub-bands or temporal segments. Experiments performed on the ASVspoof 2019 logical access database show that our GAT-based model with temporal attention outperforms all of our baseline single systems. Furthermore, GAT-based systems are complementary to a set of existing systems. The fusion of GAT-based models with more conventional countermeasures delivers a 47% relative improvement in performance compared to the best performing single GAT system.

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“…Since not all study was evaluated using the same criterion, this creates a problem when comparing the works. Hence, some recent works such as [62,145], and [140] provided more than one evaluation criteria for performance comparison. A fair comparison of voice PAD in terms of performance may be made through the standardization of evaluation criteria.…”

Section: Evaluation Criteriamentioning

confidence: 99%

A survey on presentation attack detection for automatic speaker verification systems: State-of-the-art, taxonomy, issues and future direction

Tan

Hijazi

Khamis

et al. 2021

Multimed Tools Appl

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The emergence of biometric technology provides enhanced security compared to the traditional identification and authentication techniques that were less efficient and secure. Despite the advantages brought by biometric technology, the existing biometric systems such as Automatic Speaker Verification (ASV) systems are weak against presentation attacks. A presentation attack is a spoofing attack launched to subvert an ASV system to gain access to the system. Though numerous Presentation Attack Detection (PAD) systems were reported in the literature, a systematic survey that describes the current state of research and application is unavailable. This paper presents a systematic analysis of the state-of-the-art voice PAD systems to promote further advancement in this area. The objectives of this paper are two folds: (i) to understand the nature of recent work on PAD systems, and (ii) to identify areas that require additional research. From the survey, a taxonomy of voice PAD and the trend analysis of recent work on PAD systems were built and presented, whereby the recent and relevant articles including articles from Interspeech and ICASSP Conferences, mostly indexed by Scopus, published between 2015 and 2021 were considered. A total of 172 articles were surveyed in this work. The findings of this survey present the limitation of recent works, which include spoof-type dependent PAD. Consequently, the future direction of work on voice PAD for interested researchers is established. The findings of this survey present the limitation of recent works, which include spoof-type dependent PAD. Consequently, the future direction of work on voice PAD for interested researchers is established.

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Siamese Convolutional Neural Network Using Gaussian Probability Feature for Spoofing Speech Detection

Cited by 23 publications

References 15 publications

Audio Deepfake Detection Based on a Combination of F0 Information and Real Plus Imaginary Spectrogram Features

Audio Deepfake Detection Based on a Combination of F0 Information and Real Plus Imaginary Spectrogram Features

Graph Attention Networks for Anti-Spoofing

A survey on presentation attack detection for automatic speaker verification systems: State-of-the-art, taxonomy, issues and future direction

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