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Готові списки джерел за темами / ASVspoof

Добірка наукової літератури з теми "ASVspoof"

Автор: Grafiati

Опубліковано: 7 липня 2024

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Тези доповідей конференцій

Статті в журналах з теми "ASVspoof":

1

Nafees, Muhammad, Abid Rauf, and Rabbia Mahum. "Automatic Spoofing Detection Using Deep Learning." Global Social Sciences Review IX, no. I (March 30, 2024): 111–333. http://dx.doi.org/10.31703/gssr.2024(ix-i).11.

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Анотація:

Deep fakes stand out to be the most dangerous side effects of Artificial Intelligence. AI assists to produce voice cloning of any entity which is very arduous to categorize whether it’s fake or real. The aim of the research is to impart a spoofing detection system to an automatic speaker verification (ASV) system that can perceive false voices efficiently. The goal is to perceive the unapparent audio elements with maximum precision and to develop a model that is proficient in automatically extracting audio features by utilizing the ASVspoof 2019 dataset. Hence, the proposed ML-DL SafetyNet model is designed that delicately differentiate ASVspoof 2019 dataset voice speeches into fake or bonafide. ASVspoof 2019 dataset is characterized into two segments LA and PA. The ML-DL SafetyNet model is centred on two unique processes; deep learning and machine learning classifiers. Both techniques executed strong performance by achieving an accuracy of 90%.

2

Zhang, Jiachen, Guoqing Tu, Shubo Liu, and Zhaohui Cai. "Audio Anti-Spoofing Based on Audio Feature Fusion." Algorithms 16, no. 7 (June 28, 2023): 317. http://dx.doi.org/10.3390/a16070317.

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The rapid development of speech synthesis technology has significantly improved the naturalness and human-likeness of synthetic speech. As the technical barriers for speech synthesis are rapidly lowering, the number of illegal activities such as fraud and extortion is increasing, posing a significant threat to authentication systems, such as automatic speaker verification. This paper proposes an end-to-end speech synthesis detection model based on audio feature fusion in response to the constantly evolving synthesis techniques and to improve the accuracy of detecting synthetic speech. The model uses a pre-trained wav2vec2 model to extract features from raw waveforms and utilizes an audio feature fusion module for back-end classification. The audio feature fusion module aims to improve the model accuracy by adequately utilizing the audio features extracted from the front end and fusing the information from timeframes and feature dimensions. Data augmentation techniques are also used to enhance the performance generalization of the model. The model is trained on the training and development sets of the logical access (LA) dataset of the ASVspoof 2019 Challenge, an international standard, and is tested on the logical access (LA) and deep-fake (DF) evaluation datasets of the ASVspoof 2021 Challenge. The equal error rate (EER) on ASVspoof 2021 LA and ASVspoof 2021 DF are 1.18% and 2.62%, respectively, achieving the best results on the DF dataset.

3

Faham Ali Zaidi, Syed, and Longting Xu. "Implementation of Multiple Feature Selection Algorithms for Speech Spoofing Detection." Journal of Physics: Conference Series 2224, no. 1 (April 1, 2022): 012119. http://dx.doi.org/10.1088/1742-6596/2224/1/012119.

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Abstract The ASVspoof challenge sequences were proposed to lead the research in anti-spoofing to a new level for automatic speaker verification (ASV). It’s verified that constant Q cepstral coefficients (CQCC) processes speech in variable frequencies with adjustable resolution and outperforms the other generally used features and Linear Frequency Cepstral Coefficient (LFCC) is used in high-frequency areas. The feature selection algorithm is offered to decrease computational complexity and overfitting for spoofed utterance detection. Precisely, there’s a demand for feature selection algorithms that are computationally effective and sensitive to feature interactions so that useful features aren’t falsely excluded during the ranking process. We experiment on the ASVspoof 2019 challenge for the assessment of spoofing countermeasures. After the evaluation of our given algorithms and data gives us an equal error rate (EER) and tandem discovery cost function (t-DCF) values. Experimental results on ASVspoof 2019 physical access referring to multiple feature selection approaches show a breakthrough compared to the baseline.

4

Yang, Jichen, Qianhua He, Yongjian Hu, and Weiqiang Pan. "CBC-Based Synthetic Speech Detection." International Journal of Digital Crime and Forensics 11, no. 2 (April 2019): 63–74. http://dx.doi.org/10.4018/ijdcf.2019040105.

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Анотація:

In previous studies of synthetic speech detection (SSD), the most widely used features are based on a linear power spectrum. Different from conventional methods, this article proposes a new feature extraction method for SSD from octave power spectrum which is obtained from constant-Q transform (CQT). By combining CQT, block transform (BT) and discrete cosine transform (DCT), a new feature is obtained, namely, constant-Q block coefficients (CBC). In which, CQT is used to transform speech from the time domain into the frequency domain, BT is used to segment octave power spectrum into many blocks and DCT is used to extract principal information of every block. The experimental results on ASVspoof 2015 corpus shows that CBC is superior to other front-ends features that have been benchmarked on ASVspoof 2015 evaluation set in terms of equal error rate (EER).

5

Wu, Zhizheng, Junichi Yamagishi, Tomi Kinnunen, Cemal Hanilci, Mohammed Sahidullah, Aleksandr Sizov, Nicholas Evans, Massimiliano Todisco, and Hector Delgado. "ASVspoof: The Automatic Speaker Verification Spoofing and Countermeasures Challenge." IEEE Journal of Selected Topics in Signal Processing 11, no. 4 (June 2017): 588–604. http://dx.doi.org/10.1109/jstsp.2017.2671435.

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6

Phapatanaburi, Khomdet, Prawit Buayai, Watcharaphon Naktong, and Jakkree Srinonchat. "Exploiting Magnitude and Phase Aware Deep Neural Network for Replay Attack Detection." ECTI Transactions on Electrical Engineering, Electronics, and Communications 18, no. 2 (August 31, 2020): 89–97. http://dx.doi.org/10.37936/ecti-eec.2020182.240341.

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Magnitude and phase aware deep neural network (MP aware DNN) based on Fast Fourier Transform information, has recently been received more attention to many speech applications. However, little attention has been paid to its aspect in terms of replay attack detection developed for the automatic speaker verification and countermeasures (ASVspoof 2017). This paper aims to investigate the MP aware DNN as a speech classification for detecting non-replayed (genuine) and replayed speech. Also, to exploit the advantage of the classifier-based complementary to improve the reliable detection decision, we propose a novel method by combining MP aware DNN with standard replay attack detection (that is, the use of constant Q transform cepstral coefficients-based Gaussian mixture model classification: CQCC-based GMM). Experiments are evaluated using ASVspoof 2017 and a standard measure of detection performance called equal error rate (EER). The results showed that MP aware DNN -based detection performed conventional DNN method using only the magnitude/phase features. Moreover, we found that score combination of CQCC-based GMM with MP aware DNN achieved additional improvement, indicating that MP aware DNN is very useful, especially when combined with the CQCC-based GMM for replay attack detection.

7

Tan, Choon Beng, Mohd Hanafi Ahmad Hijazi, Frazier Kok, Mohd Saberi Mohamad, and Puteri Nor Ellyza Nohuddin. "Artificial speech detection using image-based features and random forest classifier." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 1 (March 1, 2022): 161. http://dx.doi.org/10.11591/ijai.v11.i1.pp161-172.

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Анотація:

The ASVspoof 2015 Challenge was one of the efforts of the research community in the field of speech processing to foster the development of generalized countermeasures against spoofing attacks. However, most countermeasures submitted to the ASVspoof 2015 Challenge failed to detect the S10 attack effectively, the only attack that was generated using the waveform concatenation approach. Hence, more informative features are needed to detect previously unseen spoofing attacks. This paper presents an approach that uses data transformation techniques to engineer image-based features together with random forest classifier to detect artificial speech. The objectives are two-fold: (i) to extract image-based features from the melfrequency cepstral coefficients representation of the speech signal and (ii) to compare the performance of using the extracted features and Random Forest to determine the authenticity of voices with the existing approaches. An audio-to-image transformation technique was used to engineer new features in classifying genuine and spoof voices. An experiment was conducted to find the appropriate combination of the engineered features and classifier. Experimental results showed that the proposed approach was able to detect speech synthesis and voice conversion attacks effectively, with an equal error rate of 0.10% and accuracy of 99.93%.

8

Hu, Chenlei, Ruohua Zhou, and Qingsheng Yuan. "Replay Speech Detection Based on Dual-Input Hierarchical Fusion Network." Applied Sciences 13, no. 9 (April 25, 2023): 5350. http://dx.doi.org/10.3390/app13095350.

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Speech anti-spoofing is a crucial aspect of speaker recognition systems and has received a great deal of attention in recent years. Deep neural networks have achieved satisfactory results in datasets with similar training and testing data distributions, but their generalization ability is limited in datasets with different distributions. In this paper, we proposed a novel dual-input hierarchical fusion network (HFN) to improve the generalization ability of our model. The network had two inputs (the original speech signal and the time-reversed signal), which increased the volume and diversity of the training data. The hierarchical fusion model (HFM) enabled more thorough fusion of information from different input levels and improved model performance by fusing the two inputs after speech feature extraction. We finally evaluated the results using the ASVspoof 2021 PA (Physical Access) dataset, and the proposed system achieved an Equal Error Rate (EER) of 24.46% and a minimum tandem Detection Cost Function (min t-DCF) of 0.6708 in the test set. Compared with the four baseline systems in the ASVspoof 2021 competition, the proposed system min t-DCF values were decreased by 28.9%, 31.0%, 32.6%, and 32.9%, and the EERs were decreased by 35.7%, 38.1%, 45.4%, and 49.7%, respectively.

9

Adiban, Mohammad, Hossein Sameti, and Saeedreza Shehnepoor. "Replay spoofing countermeasure using autoencoder and siamese networks on ASVspoof 2019 challenge." Computer Speech & Language 64 (November 2020): 101105. http://dx.doi.org/10.1016/j.csl.2020.101105.

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10

Nautsch, Andreas, Xin Wang, Nicholas Evans, Tomi H. Kinnunen, Ville Vestman, Massimiliano Todisco, Hector Delgado, Md Sahidullah, Junichi Yamagishi, and Kong Aik Lee. "ASVspoof 2019: Spoofing Countermeasures for the Detection of Synthesized, Converted and Replayed Speech." IEEE Transactions on Biometrics, Behavior, and Identity Science 3, no. 2 (April 2021): 252–65. http://dx.doi.org/10.1109/tbiom.2021.3059479.

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Більше джерел

Дисертації з теми "ASVspoof":

1

Ge, Wanying. "Spoofing-robust Automatic Speaker Verification : Architecture, Explainability and Joint Optimisation." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS071.

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Cette thèse explore les systèmes de vérification automatique du locuteur (ASV) et leurs vulnérabilités aux attaques de spoofing, soulignant la nécessité de contre-mesures robustes contre le spoofing (CMs). Elle présente l'application de la recherche d'architecture différentiable partiellement connectée (PC-DARTS) pour optimiser les architectures de réseau pour la lutte contre l'usurpation de la voix, démontrant une performance compétitive et une meilleure généralisation contre les attaques non vues. En outre, il utilise SHapley Additive exPlanations (SHAP) pour analyser et visualiser l'impact des caractéristiques d'entrée individuelles sur les performances de détection, ce qui permet de mieux comprendre le comportement du système et les caractéristiques de l'attaque. Enfin, il propose un système intégré de vérification du locuteur sensible à l'usurpation, en soulignant les avantages et les défis de l'optimisation conjointe de l'ASV et des CM pour améliorer les capacités de détection et la robustesse du système contre les attaques d'usurpation
This thesis explores Automatic Speaker Verification (ASV) systems and their vulnerabilities to spoofing attacks, highlighting the necessity for robust spoofing countermeasures (CMs). It introduces the application of Partially Connected Differentiable Architecture Search (PC-DARTS) for optimizing network architectures for voice anti-spoofing, demonstrating competitive performance and better generalization against unseen attacks. Further, it employs SHapley Additive exPlanations (SHAP) to analyse and visualise the impact of individual input features on detection performance, providing insights into system behaviour and attack characteristics. Lastly, it proposes an integrated spoofing-aware speaker verification system, emphasizing the benefits and challenges of joint optimization of ASV and CMs for enhanced detection capabilities and system robustness against spoofing attacks

Тези доповідей конференцій з теми "ASVspoof":

1

Yamagishi, Junichi. "Lessons Learned from ASVSpoof and Remaining Challenges." In MM '22: The 30th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3552466.3554359.

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2

Chen, Tianxiang, Elie Khoury, Kedar Phatak, and Ganesh Sivaraman. "Pindrop Labs' Submission to the ASVspoof 2021 Challenge." In 2021 Edition of the Automatic Speaker Verification and Spoofing Countermeasures Challenge. ISCA: ISCA, 2021. http://dx.doi.org/10.21437/asvspoof.2021-14.

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3

Feng, Zhimin, Qiqi Tong, Yanhua Long, Shuang Wei, Chunxia Yang, and Qiaozheng Zhang. "SHNU Anti-spoofing Systems for ASVspoof 2019 Challenge." In 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2019. http://dx.doi.org/10.1109/apsipaasc47483.2019.9023319.

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4

Novoselov, Sergey, Alexandr Kozlov, Galina Lavrentyeva, Konstantin Simonchik, and Vadim Shchemelinin. "STC anti-spoofing systems for the ASVspoof 2015 challenge." In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. http://dx.doi.org/10.1109/icassp.2016.7472724.

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5

Benhafid, Zhor, Sid Ahmed Selouani, Mohammed Sidi Yakoub, and Abderrahmane Amrouche. "LARIHS ASSERT Reassessment for Logical Access ASVspoof 2021 Challenge." In 2021 Edition of the Automatic Speaker Verification and Spoofing Countermeasures Challenge. ISCA: ISCA, 2021. http://dx.doi.org/10.21437/asvspoof.2021-15.

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6

Cáceres, Joaquín, Roberto Font, Teresa Grau, and Javier Molina. "The Biometric Vox System for the ASVspoof 2021 Challenge." In 2021 Edition of the Automatic Speaker Verification and Spoofing Countermeasures Challenge. ISCA: ISCA, 2021. http://dx.doi.org/10.21437/asvspoof.2021-11.

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7

Todisco, Massimiliano, Xin Wang, Ville Vestman, Md Sahidullah, Héctor Delgado, Andreas Nautsch, Junichi Yamagishi, Nicholas Evans, Tomi H. Kinnunen, and Kong Aik Lee. "ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection." In Interspeech 2019. ISCA: ISCA, 2019. http://dx.doi.org/10.21437/interspeech.2019-2249.

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8

Delgado, Héctor, Massimiliano Todisco, Md Sahidullah, Nicholas Evans, Tomi Kinnunen, Kong Aik Lee, and Junichi Yamagishi. "ASVspoof 2017 Version 2.0: meta-data analysis and baseline enhancements." In Odyssey 2018 The Speaker and Language Recognition Workshop. ISCA: ISCA, 2018. http://dx.doi.org/10.21437/odyssey.2018-42.

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9

Yamagishi, Junichi, Xin Wang, Massimiliano Todisco, Md Sahidullah, Jose Patino, Andreas Nautsch, Xuechen Liu, et al. "ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection." In 2021 Edition of the Automatic Speaker Verification and Spoofing Countermeasures Challenge. ISCA: ISCA, 2021. http://dx.doi.org/10.21437/asvspoof.2021-8.

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10

Chen, Xinhui, You Zhang, Ge Zhu, and Zhiyao Duan. "UR Channel-Robust Synthetic Speech Detection System for ASVspoof 2021." In 2021 Edition of the Automatic Speaker Verification and Spoofing Countermeasures Challenge. ISCA: ISCA, 2021. http://dx.doi.org/10.21437/asvspoof.2021-12.

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