Trident of Poseidon: A Generalized Approach for Detecting Deepfake Voices

Deepfakes, an increasingly prevalent form of information attack, pose serious threats to security and privacy. Deepfake voice attacks, in particular, have the potential to cause widespread disruption, creating an urgent need for an effective detection system. In this research, we propose the Trident of Poseidon - a novel set of triad training strategies aimed at enhancing the generalizability of deepfake voice detection models. Our solution comprises three key components: (1) Supervised Contrastive Learning, (2) Hard Negative Mining by Audio Re-synthesizing, and (3) Effective Proactive Batch Sampling. Together, these enable the model to learn more robust features. Our extensive experiments demonstrate that our approach outperforms existing methods in both in-domain and out-of-domain testing scenarios, making significant strides toward securing digital media against deepfake voice attacks.