METHOD FOR CROSS-DOMAIN SPEECH DEEPFAKE DETECTION

A method for detecting speech deepfakes under real-world conditions is disclosed, where factors such as audio compression, transmission channels, and codec transformations may distort the speech signal. The invention combines self-supervised learning pre-trained models, domain-invariant representation learning, and one-class learning to improve the robustness and generalization of deepfake detection systems. By aligning feature distributions across domains, the system ensures consistent representation of genuine speech, even under varying conditions, and effectively identifies synthetic speech. The proposed method offering a robust solution for real-world audio deepfake detection applications.