Replay spoofing countermeasure using autoencoder and siamese networks on ASVspoof 2019 challenge

Adiban, Mohammad; Sameti, Hossein; Shehnepoor, Saeedreza

doi:10.1016/j.csl.2020.101105

Cited by 20 publications

(7 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As replay attacks do not require specialized knowledge, the threat of a replay attack can be considered more significant compared to voice conversion and speech synthesis attacks. Some recent works on replay attack detection are [3,57,92]. Some works employ a combination of all attacks described above.…”

Section: Types Of Presentation Attackmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: Types Of Presentation Attackmentioning

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

View full text Add to dashboard Cite

show abstract

“…Compared with the traditional CNN, the light CNN has fewer parameters, which can alleviate the overfitting problems in small datasets. In [22], an autoencoder was trained to reduce the dimension of CQCC. The bottleneck activations of the autoencoder were fed into a Siamese neural network for classification.…”

Section: I R E L a T E D W O R Kmentioning

confidence: 99%

A multi-branch ResNet with discriminative features for detection of replay speech signals

Cheng

Zheng

2020

SIP

View full text Add to dashboard Cite

Nowadays, the security of ASV systems is increasingly gaining attention. As one of the common spoofing methods, replay attacks are easy to implement but difficult to detect. Many researchers focus on designing various features to detect the distortion of replay attack attempts. Constant-Q cepstral coefficients (CQCC), based on the magnitude of the constant-Q transform (CQT), is one of the striking features in the field of replay detection. However, it ignores phase information, which may also be distorted in the replay processes. In this work, we propose a CQT-based modified group delay feature (CQTMGD) which can capture the phase information of CQT. Furthermore, a multi-branch residual convolution network, ResNeWt, is proposed to distinguish replay attacks from bonafide attempts. We evaluated our proposal in the ASVspoof 2019 physical access dataset. Results show that CQTMGD outperformed the traditional MGD feature, and the fusion with other magnitude-based and phase-based features achieved a further improvement. Our best fusion system achieved 0.0096 min-tDCF and 0.39% EER on the evaluation set and it outperformed all the other state-of-the-art methods in the ASVspoof 2019 physical access challenge.

show abstract

“…Many algorithms and methods based on using different features and classifiers have been proposed [2,3,4,5,6,7]. For methods based on a single feature, in the ASVspoof 2019 challenge, the baselines are methods using either constant-Q cepstral co-efficients (CQCCs) or linear-frequency cepstral coefficients (LFCCs) as features with a Gaussian mixture model (GMM) as the classifier [2].…”

Section: Introductionmentioning

confidence: 99%

Replay Attack Detection in Automatic Speaker Verification Using Gammatone Cepstral Coefficients and ResNet-Based Model

Chaiwongyen

Duangpummet

Karnjana

et al. 2022

Journal of Signal Processing

View full text Add to dashboard Cite

This paper presents a method for detecting replay attacks in an automatic speaker verification system. The replay attack is of interest because it is the most straightforward, effective attack, and difficult to detect. Even though many speech features and classifiers have been proposed, the detection performance, such as an equal error rate (EER), accuracy, and balanced accuracy, need to be improved. Therefore, we propose a method for replay attack detection that applies the Gammatone cepstral coefficients with a ResNet-based model. The proposed method was evaluated and compared with existing methods and baselines in the ASVspoof 2019 challenge. The results indicated that the proposed method outperforms our previous method and the baselines in which the EER was 8.4%. In addition, the accuracy and balanced accuracy of the spoofing detection were improved.

show abstract

Replay spoofing countermeasure using autoencoder and siamese networks on ASVspoof 2019 challenge

Cited by 20 publications

References 17 publications

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

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

A multi-branch ResNet with discriminative features for detection of replay speech signals

Replay Attack Detection in Automatic Speaker Verification Using Gammatone Cepstral Coefficients and ResNet-Based Model

Contact Info

Product

Resources

About