Congyan Lang

Single vision foundation models inevitably overfit to deepfake artifacts, but dynamically arbitrating between CLIP's semantics and DINO's structural features via prediction entropy delivers state-of-the-art generalization across unseen generators.

A multi-agent forensic reasoning framework outperforms leading closed-source models in deepfake detection by leveraging diverse analytical perspectives on forgery cues.

Bidirectional reasoning in CZSL can significantly reduce prediction errors, leading to state-of-the-art performance in recognizing unseen attribute-object pairs.