Explicit Correlation Learning for Generalizable Cross-Modal Deepfake Detection

Understand

With the rising prevalence of deepfakes, there is a growing interest in developing generalizable detection methods for various types of deepfakes.

• While effective in their specific modalities, traditional detection methods fall short in addressing the generalizability of detection across diverse cross-modal deepfakes.
• This paper aims to explicitly learn potential cross-modal correlation to enhance deepfake detection towards various generation scenarios.
• Our approach introduces a correlation distillation task, which models the inherent cross-modal correlation based on content information.