Replay Attack Detection Using Linear Prediction Analysis-Based Relative Phase Features

Phapatanaburi, Khomdet; Wang, Longbiao; Nakagawa, Seiichi; Iwahashi, Masahiro

doi:10.1109/access.2019.2960369

Cited by 18 publications

(8 citation statements)

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“…In the future, by getting inspired by [36], we have a plan to use new neuroheadsets such as Emotiv EPOC+ and Open BCI neuroheadsets instead of EPOC neuroheadset with the aim of further improving the performance. We would also like to combine the phase feature extraction [31,37] and the neural network-based bottleneck feature extraction [38] with the proposed system in the future.…”

Section: Discussionmentioning

confidence: 99%

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Brainwave Classification for Character-Writing Application using EMD-based GMM and KELM approaches

Phapatanaburi¹,

kokkhunthod²,

Wang³

et al. 2021

Computers, Materials &Amp; Continua

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A brainwave classification, which does not involve any limb movement and stimulus for character-writing applications, benefits impaired people, in terms of practical communication, because it allows users to command a device/computer directly via electroencephalogram signals. In this paper, we propose a new framework based on Empirical Mode Decomposition (EMD) features along with the Gaussian Mixture Model (GMM) and Kernel Extreme Learning Machine (KELM)-based classifiers. For this purpose, firstly, we introduce EMD to decompose EEG signals into Intrinsic Mode Functions (IMFs), which actually are used as the input features of the brainwave classification for the character-writing application. We hypothesize that EMD along with the appropriate IMF is quite powerful for the brainwave classification, in terms of character applications, because of the wavelet-like decomposition without any down sampling process. Secondly, by getting motivated with shallow learning classifiers, we can provide promising performance for the classification of binary classes, GMM and KELM, which are applied for the learning of features along with the brainwave classification. Lastly, we propose a new method by combining GMM and KELM to fuse the merits of different classifiers. Moreover, the proposed methods are validated by using the volunteer-independent 5-fold cross-validation and accuracy as a standard measurement. The experimental results showed that EMD with the proper IMF achieved better results than the conventional discrete wavelet transform (DWT) feature. Moreover, we found that the EMD feature along with the GMM/KELM-based classifier provides the average accuracy of 77.40% and 80.10%, respectively, which could perform better than the conventional methods where we use DWT along with the artificial neural network classifier in order to get the average accuracy of 80.60%. Furthermore, we obtained the improved performance by combining GMM and KELM, i.e., average accuracy of 80.60%. These outcomes exhibit the usefulness of the EMD

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Section: Discussionmentioning

confidence: 99%

“…Score combination gives a mechanism to fuse the merits of different classifiers in order to increase the decision performance. It has been adopted in many applications [16,30,31]. In this paper, the score combination is also used in our experiment.…”

Section: Score Combination Of Gmm and Kelmmentioning

confidence: 99%

Brainwave Classification for Character-Writing Application using EMD-based GMM and KELM approaches

Phapatanaburi¹,

kokkhunthod²,

Wang³

et al. 2021

Computers, Materials &Amp; Continua

Self Cite

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show abstract

“…Oo et al [19] introduced the relative phase (RP) feature and further extended it in the Mel-scale (Mel-RP) and the gammatone-scale (Gamma-RP). Phapatanaburi et al [20] proposed to extract RP based on the linear prediction analysis (LPA) , which extracted RP on the residual signal of LPA.…”

Section: Related Workmentioning

confidence: 99%

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

Cheng

Zheng

2020

SIP

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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.

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“…In Yu, Tan, Ma, Martin, and Guo (2017) authors use different types of filter banks such as linear, Gammatone, and its inverted version and also inverted Mel filter banks to extract different variations of cepstral coefficients for replay attack detection. Furthermore, recently, linear prediction residual‐based features like linear prediction residual magnitude cepstral coefficients (Hanilç, 2018), linear prediction residual phase cepstral coefficients (Hanilç, 2018), LP‐based relative phase features (Phapatanaburi, Wang, Nakagawa, & Iwahashi, 2019), and residual Mel frequency cepstral coefficients (Mishra, Singh, & Pati, 2018), have shown interesting and promising results in detection of spoofed speech.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary fusion of classifiers trained on linear prediction based features for replay attack detection

Nasersharif

Yazdani

2021

Expert Systems

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Recently, linear prediction analysis (LP) related features have been successfully used for replay attack detection due to the imperfection in the LP‐based signal produced by recording and playback devices. In this paper, we propose a weighted linear combination of classifier scores for replay attack detection where our classifiers, including Gaussian mixture models (GMMs) and support vector machines (SVMs), are trained on a variety of LP and LP residual‐based features. In this way, we can benefits from all of the LP‐related features when we combine classifiers trained on these features. We determine classifier weights using two evolutionary algorithms: genetic algorithm and particle swarm optimization. Furthermore, we propose a new feature based on performing LP residuals analysis of Mel sub‐band energies. We also propose a deep structure for extracting deep features from LP‐based coefficients to consider the class labels (genuine or spoofed speaker) in the feature extraction process. Results of our classifier system on the ASVspoof 2017 version 2 dataset show equal error rates of 0.3% and 4.8% for its development and evaluation subset, respectively. We also applied our proposed replay attack detection method to an ASV system that has acceptable results.

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Replay Attack Detection Using Linear Prediction Analysis-Based Relative Phase Features

Cited by 18 publications

References 50 publications

Brainwave Classification for Character-Writing Application using EMD-based GMM and KELM approaches

Brainwave Classification for Character-Writing Application using EMD-based GMM and KELM approaches

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

Evolutionary fusion of classifiers trained on linear prediction based features for replay attack detection

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