Learning Generalizable Representations for Deepfake Detection With Realistic Sample Generation and Dual Augmentation

Summary

Researchers propose RSG-DA, a framework for deepfake detection that targets poor generalization to unknown images and novel forgery types. It combines a Dynamic Landmark Diffusion Generator that synthesizes hybrid forgery samples, a Dual Data Augmentation strategy, and a Lightweight Generic Forgery Distillation module. Experiments report consistent gains over state-of-the-art methods in intra-dataset and cross-dataset evaluations.

Topics

• Deepfakes and impersonation