GFADE: generalized feature adaptation and discrimination enhancement for deepfake detection.

ABSTRACT: With the rapid advancement of deep generative techniques, such as generative adversarial networks (GANs), the creation of realistic fake images and videos has become increasingly accessible, raising significant security and privacy concerns. Although existing deepfake detection methods perform well within a single dataset, they often experience substantial performance degradation when applied across datasets or manipulation types. To address this challenge, we propose a novel deepfake detection framework that combines multiple loss functions and the MixStyle technique. By integrating Cross-Entropy Loss, ArcFace loss, and Focal Loss, our model enhances its discriminative power to better handle complex forgery characteristics and effectively mitigate data imbalance. Additionally, the MixStyl

SUBMITTER: Tian Z 

PROVIDER: S-EPMC12192638 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature