ADVANCES IN DEEPFAKE VOICE DETECTION: TECHNICAL APPROACHES, RISKS, AND ETHICAL PERSPECTIVES

Abstract

Deepfake voice technologies represent a transformative advancement in artificial intelligence, particularly in speech synthesis and voice cloning. Leveraging deep learning architectures such as Generative Adversarial Networks (GANs) and autoencoders, these systems can produce highly realistic synthetic voices that closely resemble human speech. While offering benefits in domains such as accessibility, entertainment, and personalized services, deepfake voices also pose significant risks, including misinformation, identity theft, and cybercrime. This paper examines both the generation methods and detection strategies for synthetic speech, with a focus on neural network–based approaches to voice authentication and deepfake recognition. Furthermore, it discusses the ethical and legal challenges associated with deepfake voice technologies, emphasizing issues of consent, digital trust, and privacy. By critically reviewing recent advancements and proposing a structured framework for detection, this study seeks to contribute to the development of secure, transparent, and resilient solutions against malicious voice manipulation.