ANALYSIS OF DEEPFAKE AUDIO IDENTIFICATION METHODS

The rapid development of artificial intelligence has significantly improved speech synthesis and voice cloning technologies. Modern text-to-speech (TTS) and voice conversion (VC) systems are capable of generating highly realistic synthetic speech that is often indistinguishable from genuine human speech. While these technologies offer numerous beneficial applications, they also create serious security threats, including identity theft, financial fraud, misinformation campaigns, and social engineering attacks. Consequently, the identification of deepfake audio has become a critical research area in digital forensics and cybersecurity. This paper presents a comprehensive analysis of contemporary deepfake audio identification methods, including spectral analysis, speaker verification, physiological speech analysis, prosodic feature extraction, watermark detection, and deep learning-based detection approaches. The strengths, limitations, and future challenges of each method are discussed. The study highlights the ongoing technological competition between audio synthesis systems and detection mechanisms and emphasizes the need for robust and adaptive identification frameworks[1].