Author:
C Kavitha,G Pavan,Kayyaniyil Joby Josh,Nayak R Vipul,Rathod Rakesh
Abstract
Deepfake technology has made it increasingly difficult to discern real from fabricated audio, posing a significant challenge in the digital age. By employing sophisticated algorithms and voice recognition techniques, the system proposed in this article can analyse voice patterns and nuances to spot inconsistencies and anomalies, which are common indicators of deepfake voices and prevent scams and other types of cyber security issues.
Publisher
International Journal of Innovative Science and Research Technology
Reference17 articles.
1. A. Hamza, A. R. Javed, F. Iqbal, et al., “Deepfake audio detection via MFCC features using machine learning,” Access, IEEE, 2022.
2. K. Li, X. Lu, M. Akagi, et al., “Contributions of jitter and shimmer in the voice for fake audio detection,” Access, IEEE, 2023.
3. D. U. Leonzio, L. Cuccovillo, P. Bestagini, et al., “Audio splicing detection and localization based on acquisition device traces,” J. Sel. Topics Signal Process., vol. 13, no. 5, pp. 1084–1097, 2019.
4. A. Abbasi, A. R. R. Javed, A. Yasin, Z. Jalil, N. Kryvinska, and U. Tariq, ‘‘A large-scale benchmark dataset for anomaly detection and rare event classification for audio forensics,’’ IEEE Access, vol. 10, pp. 38885–38894, 2022.
5. A. R. Javed, W. Ahmed, M. Alazab, Z. Jalil, K. Kifayat, and T. R. Gadekallu, ‘‘A comprehensive survey on computer forensics: Stateof-the-art, tools, techniques, challenges, and future directions,’’ IEEE Access, vol. 10, pp. 11065–11089, 2022.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献