Real-Time Detection of AI-Generated Deepfake Audio: A Novel Approach

The rise of AI-generated deepfake audio presents a significant challenge in audio forensics, as traditional detection methods are inadequate against sophisticated manipulations. This study proposes a novel approach combining deep learning algorithms with signal processing techniques to detect deepfake audio in real-time. By leveraging voice pattern recognition and anomaly detection, the method achieves high accuracy and low latency. Experimental results show a significant improvement in detection performance, with the integrated system outperforming traditional methods. The proposed solution demonstrates potential for enhancing security and digital forensics by enabling the swift identification of deepfake audio.