An Optimized and Privacy-Preserving System Architecture for Effective Voice Authentication over Wireless Network

Main Article Content

Dr. Aniruddha Deka
Dr. Debashis Dev Misra

Abstract

The speaker authentication systems assist in determining the identity of speaker in audio through distinctive voice characteristics. Accurate speaker authentication over wireless network is becoming more challenging due to phishing assaults over the network. There have been constructed multiple kinds of speech authentication models to employ in multiple applications where voice authentication is a primary focus for user identity verification. However, explored voice authentication models have some limitations related to accuracy and phishing assaults in real-time over wireless network. In research, optimized and privacy-preserving system architecture for effective speaker authentication over a wireless network has been proposed to accurately identify the speaker voice in real-time and prevent phishing assaults over network in more accurate manner. The proposed system achieved very good performance metrics measured accuracy, precision, and recall and the F1 score of the proposed model were98.91%, 96.43%, 95.37%, and 97.99%, respectively. The measured training losses on the epoch 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100 were 2.4, 2.1, 1.8, 1.5, 1.2, 0.9, 0.6, 0.3, 0.3, 0.3, and 0.2, respectively. Also, the measured testing losses on the epoch of 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100 were 2.2, 2, 1.5, 1.4, 1.1, 0.8, 0.8, 0.7, 0.4, 0.1 and 0.1, respectively. Voice authentication over wireless networks is serious issue due to various phishing attacks and inaccuracy in voice identification. Therefore, this requires huge attention for further research in this field to develop less computationally complex speech authentication systems

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[1]
Dr. Aniruddha Deka and Dr. Debashis Dev Misra , Trans., “An Optimized and Privacy-Preserving System Architecture for Effective Voice Authentication over Wireless Network”, IJRTE, vol. 12, no. 3, pp. 1–9, Oct. 2023, doi: 10.35940/ijrte.C7862.0912323.
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How to Cite

[1]
Dr. Aniruddha Deka and Dr. Debashis Dev Misra , Trans., “An Optimized and Privacy-Preserving System Architecture for Effective Voice Authentication over Wireless Network”, IJRTE, vol. 12, no. 3, pp. 1–9, Oct. 2023, doi: 10.35940/ijrte.C7862.0912323.
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References

P. Dhakal, P. Damacharla, A. Y. Javaid, and V. Devabhaktuni, “A Near Real-Time Automatic Speaker Recognition Architecture for Voice-Based User Interface,” Mach. Learn. Knowl. Extr., vol. 1, no. 1, pp. 504–520, 2019, doi: 10.3390/make1010031. [CrossRef]

A. V. Amrutha, K. H. Anagha, A. Kamal K, and B. Kumaraswamy, “Multi-level Speaker Authentication: An Overview and Implementation,” in 2020 IEEE 17th India Council International Conference, INDICON 2020, 2020. doi: 10.1109/INDICON49873.2020.9342423. [CrossRef]

S. Abhishek Anand, J. Liu, C. Wang, M. Shirvanian, N. Saxena, and Y. Chen, “EchoVib: Exploring Voice Authentication via Unique Non-Linear Vibrations of Short Replayed Speech,” in ASIA CCS 2021 - Proceedings of the 2021 ACM Asia Conference on Computer and Communications Security, 2021. doi: 10.1145/3433210.3437518. [CrossRef]

B. Chettri, “Voice Biometric System Security: Design and Analysis of Countermeasures for Replay Attacks,” 2020. [CrossRef]

N. Kobayashi and T. Morooka, “Application of High-accuracy Silent Speech BCI to Biometrics using Deep Learning,” in 9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021, 2021. doi: 10.1109/BCI51272.2021.9385338. [CrossRef]

S. Kinkiri, W. J. C. Melis, and S. Keates, “Machine learning for voice recognition,” Second Medw. Eng. Conf. Syst. Effic. Sustain. Model., 2017.

L. Chowdhury, M. Kamal, N. Hasan, and N. Mohammed, “Curricular SincNet: Towards Robust Deep Speaker Recognition by Emphasizing Hard Samples in Latent Space,” in BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021. doi: 10.1109/BIOSIG52210.2021.9548296. [CrossRef]

R. Jahangir et al., “Text-Independent Speaker Identification through Feature Fusion and Deep Neural Network,” IEEE Access, 2020, doi: 10.1109/ACCESS.2020.2973541. [CrossRef]

S. Duraibi, W. Alhamdani, and F. T. Sheldon, “Voice Feature Learning using Convolutional Neural Networks Designed to Avoid Replay Attacks,” in 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, 2020. doi: 10.1109/SSCI47803.2020.9308489. [CrossRef]

D. R. KS, R. MD, and S. G, “Comparative performance analysis for speech digit recognition based on MFCC and vector quantization,” Glob. Transitions Proc., 2021, doi: 10.1016/j.gltp.2021.08.013. [CrossRef]

O. Mamyrbayev, A. Akhmediyarova, A. Kydyrbekova, N. O. Mekebayev, and B. Zhumazhanov, “BIOMETRIC HUMAN AUTHENTICATION SYSTEM THROUGH SPEECH USING DEEP NEURAL NETWORKS (DNN),” Bull., 2020, doi: 10.32014/2020.2518-1467.137. [CrossRef]

M. Dua, C. Jain, and S. Kumar, “LSTM and CNN based ensemble approach for spoof detection task in automatic speaker verification systems,” J. Ambient Intell. Humaniz. Comput., 2022, doi: 10.1007/s12652-021-02960-0. [CrossRef]

S. Bunrit, T. Inkian, N. Kerdprasop, and K. Kerdprasop, “Text-independent speaker identification using deep learning model of convolution neural network,” Int. J. Mach. Learn. Comput., 2019, doi: 10.18178/ijmlc.2019.9.2.778. [CrossRef]

K. Aizat, O. Mohamed, M. Orken, A. Ainur, and B. Zhumazhanov, “Identification and authentication of user voice using DNN features and i-vector,” Cogent Eng., 2020, doi: 10.1080/23311916.2020.1751557. [CrossRef]

Q. Wang, P. Guo, and L. Xie, “Inaudible adversarial perturbations for targeted attack in speaker recognition,” in Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2020. doi: 10.21437/Interspeech.2020-1955. [CrossRef]

S. Nasr, M. Quwaider, and R. Qureshi, “Text-independent Speaker Recognition using Deep Neural Networks,” in 2021 International Conference on Information Technology, ICIT 2021 - Proceedings, 2021. doi: 10.1109/ICIT52682.2021.9491705. [CrossRef]

G. HimaBindu, G. Lakshmeeswari, G. Lalitha, and P. P. S. Subhashini, “Recognition using DNN with bacterial foraging optimization using MFCC coefficients,” J. Eur. des Syst. Autom., 2021, doi: 10.18280/JESA.540210. [CrossRef]

Y. Kang, W. Kim, S. Lim, H. Kim, and H. Seo, “DeepDetection: Privacy-Enhanced Deep Voice Detection and User Authentication for Preventing Voice Phishing,” Appl. Sci., vol. 12, no. 21, p. 11109, 2022, doi: 10.3390/app122111109. [CrossRef]

C. Z. Yang, J. Ma, S. Wang, and A. W. C. Liew, “Preventing DeepFake Attacks on Speaker Authentication by Dynamic Lip Movement Analysis,” IEEE Trans. Inf. Forensics Secur., 2021, doi: 10.1109/TIFS.2020.3045937. [CrossRef]

H. Park and T. Kim, “User Authentication Method via Speaker Recognition and Speech Synthesis Detection,” Secur. Commun. Networks, 2022, doi: 10.1155/2022/5755785. [CrossRef]

K. Khadar Nawas, M. Kumar Barik, and A. Nayeemulla Khan, “Speaker Recognition using Random Forest,” ITM Web Conf., 2021, doi: 10.1051/itmconf/20213701022. [CrossRef]

A. Mittal and M. Dua, “Automatic speaker verification systems and spoof detection techniques: review and analysis,” Int. J. Speech Technol., 2022, doi: 10.1007/s10772-021-09876-2. [CrossRef]

S. Debnath and P. Roy, “Multi-modal authentication system based on audio-visual data,” in IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2019. doi: 10.1109/TENCON.2019.8929592. [CrossRef]

V. Gujral, J. Joshi, P. Medikonda, and N. Grover, “Advanced Speech Processing for Speaker Authentication in Communication Systems,” in International Symposium on Advanced Networks and Telecommunication Systems, ANTS, 2018. doi: 10.1109/ANTS.2018.8710076. [CrossRef]

W. Jiang, Z. Wang, J. S. Jin, X. Han, and C. Li, “Speech Emotion Recognition with Heterogeneous,” pp. 1–15, 2019, doi: 10.3390/s19122730. [CrossRef]

R. Jage and S. Upadhya, “CELP and MELP speech coding techniques,” Proc. 2016 IEEE Int. Conf. Wirel. Commun. Signal Process. Networking, WiSPNET 2016, pp. 1398–1402, 2016, doi: 10.1109/WiSPNET.2016.7566366. [CrossRef]

Z. Wu et al., “ASVspoof: The Automatic Speaker Verification Spoofing and Countermeasures Challenge,” IEEE J. Sel. Top. Signal Process., vol. PP, p. 1, 2017, doi: 10.1109/JSTSP.2017.2671435. [CrossRef]

R. T. Al-Hassani, D. C. Atilla, and Ç. Aydin, “Development of High Accuracy Classifier for the Speaker Recognition System,” Appl. Bionics Biomech., 2021, doi: 10.1155/2021/5559616. [CrossRef]

Z. Hao, J. Peng, X. Dang, H. Yan, and R. Wang, “mmSafe: A Voice Security Verification System Based on Millimeter-Wave Radar,” Sensors, vol. 22, no. 23, 2022, doi: 10.3390/s22239309. [CrossRef]

A. Tahseen Ali, H. S. Abdullah, and M. N. Fadhil, “WITHDRAWN: Voice recognition system using machine learning techniques,” Mater. Today Proc., 2021, doi: https://doi.org/10.1016/j.matpr.2021.04.075. [CrossRef]

A. B. Abdusalomov, F. Safarov, M. Rakhimov, B. Turaev, and T. K. Whangbo, “Improved Feature Parameter Extraction from Speech Signals Using Machine Learning Algorithm,” Sensors, vol. 22, no. 21, p. 8122, 2022, doi: 10.3390/s22218122. [CrossRef]

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