DEEPFAKE VOICE DETECTION: METHODS, RISKS, AND ETHICAL CHALLENGES

Authors

  • Ayesha Khalid Author

Keywords:

Deepfake voice, Speech synthesis, Voice cloning, Generative Adversarial Networks (GANs), Autoencoders, Synthetic speech detection, Voice authentication, Misinformation, Digital trust, Ethical implications, AI-generated speech, Identity theft

Abstract

Deepfake voice technology, powered by deep learning models like GANs and autoencoders, enables highly realistic synthetic speech. While useful in entertainment and accessibility, it also raises serious concerns around misinformation, identity theft, and cybercrime. This paper reviews generation methods and neural network–based detection techniques for deepfake voices, emphasizing voice authentication and synthetic speech recognition. It also discusses ethical and legal issues related to consent, privacy, and digital trust. The study proposes a detection framework to strengthen defenses against malicious voice manipulation.

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Published

2024-12-31

Issue

Vol. 1 No. 1 (2024): International Journal of Computational Intelligence and Control

Section

Articles