Static–dynamic features and hybrid deep learning models based spoof detection system for ASV

Mittal, Aakshi; Dua, Mohit

doi:10.1007/s40747-021-00565-w

Cited by 17 publications

(4 citation statements)

References 37 publications

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“…Mittal et al [33] utilized CQCC at the front-end and employed two back-end models, CNN and LSTM, both individually and in combination as LSTM-CNN in their proposed work [34,35] and [33,36], respectively. Khochare et al [37] proposed a model for detecting deep fake audios, and for implementing this work fake or real datasets [38] have been used.…”

Section: Literature and Contributionmentioning

confidence: 99%

Data augmentation and hybrid feature amalgamation to detect audio deep fake attacks

Chakravarty

Dua

2023

Phys. Scr.

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The ability to distinguish between authentic and fake audio is become increasingly difficult due to the increasing accuracy of text-to-speech models, posing a serious threat to speaker verification systems. Furthermore, audio deepfakes are becoming a more likely source of deception with the development of sophisticated methods for producing synthetic voice. The ASVspoof dataset has recently been used extensively in research on the detection of audio deep fakes, together with a variety of machine and deep learning methods. The proposed work in this paper combines data augmentation techniques with hybrid feature extraction method at front-end. Two variants of audio augmentation method and Synthetic Minority Over Sampling Technique (SMOTE) have been used, which have been combined individually with Mel Frequency Cepstral Coefficients (MFCC), Gammatone Cepstral Coefficients (GTCC) and hybrid these two feature extraction methods for implementing front-end feature extraction. To implement the back-end our proposed work two deep learning models, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), and two Machine Learning (ML) classifier Random Forest (RF) and Support Vector Machine (SVM) have been used. For training, and evaluation ASVspoof 2019 Logical Access (LA) partition, and for testing of the said systems, and ASVspoof 2021 deep fake partition have been used. After analysing the results, it can be observed that combination of MFCC+GTCC with SMOTE at front-end and LSTM at back-end has outperformed all other models with 99% test accuracy, and 1.6 % Equal Error Rate (EER) over deepfake partition. Also, the testing of this best combination has been done on DEepfake CROss-lingual (DECRO) dataset. To access the effectiveness of proposed model under noisy scenarios, we have analysed our best model under noisy condition by adding Babble Noise, Street Noise and Car Noise to test data.

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Section: Literature and Contributionmentioning

confidence: 99%

Data augmentation and hybrid feature amalgamation to detect audio deep fake attacks

Chakravarty

Dua

2023

Phys. Scr.

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“…In contrast, a network combining three dense layers and three LSTM layers with MFCC features performed well, with 2.91% EER. Mittal and Dua [ 51 ] presented a hybrid deep CNN using static and dynamic CQCC features sets. Hybrid CNN combined the CNN-LSTM model with a time-distributed wrapper integrated into the LSTM network.…”

Section: Related Workmentioning

confidence: 99%

Gaussian-Filtered High-Frequency-Feature Trained Optimized BiLSTM Network for Spoofed-Speech Classification

Mewada,

Al-Asad,

Almalki

et al. 2023

Sensors

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Voice-controlled devices are in demand due to their hands-free controls. However, using voice-controlled devices in sensitive scenarios like smartphone applications and financial transactions requires protection against fraudulent attacks referred to as “speech spoofing”. The algorithms used in spoof attacks are practically unknown; hence, further analysis and development of spoof-detection models for improving spoof classification are required. A study of the spoofed-speech spectrum suggests that high-frequency features are able to discriminate genuine speech from spoofed speech well. Typically, linear or triangular filter banks are used to obtain high-frequency features. However, a Gaussian filter can extract more global information than a triangular filter. In addition, MFCC features are preferable among other speech features because of their lower covariance. Therefore, in this study, the use of a Gaussian filter is proposed for the extraction of inverted MFCC (iMFCC) features, providing high-frequency features. Complementary features are integrated with iMFCC to strengthen the features that aid in the discrimination of spoof speech. Deep learning has been proven to be efficient in classification applications, but the selection of its hyper-parameters and architecture is crucial and directly affects performance. Therefore, a Bayesian algorithm is used to optimize the BiLSTM network. Thus, in this study, we build a high-frequency-based optimized BiLSTM network to classify the spoofed-speech signal, and we present an extensive investigation using the ASVSpoof 2017 dataset. The optimized BiLSTM model is successfully trained with the least epoch and achieved a 99.58% validation accuracy. The proposed algorithm achieved a 6.58% EER on the evaluation dataset, with a relative improvement of 78% on a baseline spoof-identification system.

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“…Meng et al [31] proposed an anti-spoofing measure for smart home systems called ARRAYID which detects the passive liveness that uses the collated speech to distinguish between a live human and the replayed speech. Mittal and Dua [32], explored the deep learning models, CNN and LSTM, and the CQCC spectral feature. Two levels were used to detect spoofs.…”

Section: Literature Reviewmentioning

confidence: 99%

Voice spoofing countermeasure for voice replay attacks using deep learning

et al. 2022

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In our everyday lives, we communicate with each other using several means and channels of communication, as communication is crucial in the lives of humans. Listening and speaking are the primary forms of communication. For listening and speaking, the human voice is indispensable. Voice communication is the simplest type of communication. The Automatic Speaker Verification (ASV) system verifies users with their voices. These systems are susceptible to voice spoofing attacks - logical and physical access attacks. Recently, there has been a notable development in the detection of these attacks. Attackers use enhanced gadgets to record users’ voices, replay them for the ASV system, and be granted access for harmful purposes. In this work, we propose a secure voice spoofing countermeasure to detect voice replay attacks. We enhanced the ASV system security by building a spoofing countermeasure dependent on the decomposed signals that consist of prominent information. We used two main features— the Gammatone Cepstral Coefficients and Mel-Frequency Cepstral Coefficients— for the audio representation. For the classification of the features, we used Bi-directional Long-Short Term Memory Network in the cloud, a deep learning classifier. We investigated numerous audio features and examined each feature’s capability to obtain the most vital details from the audio for it to be labelled genuine or a spoof speech. Furthermore, we use various machine learning algorithms to illustrate the superiority of our system compared to the traditional classifiers. The results of the experiments were classified according to the parameters of accuracy, precision rate, recall, F1-score, and Equal Error Rate (EER). The results were 97%, 100%, 90.19% and 94.84%, and 2.95%, respectively.

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Static–dynamic features and hybrid deep learning models based spoof detection system for ASV

Cited by 17 publications

References 37 publications

Data augmentation and hybrid feature amalgamation to detect audio deep fake attacks

Data augmentation and hybrid feature amalgamation to detect audio deep fake attacks

Gaussian-Filtered High-Frequency-Feature Trained Optimized BiLSTM Network for Spoofed-Speech Classification

Voice spoofing countermeasure for voice replay attacks using deep learning

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