GFADE: generalized feature adaptation and discrimination enhancement for deepfake detection

Area of Science:

• Computer Science
• Artificial Intelligence
• Cybersecurity

Background:

• Deep generative models, like generative adversarial networks (GANs), enable realistic fake media creation, posing security and privacy risks.
• Current deepfake detection methods lack robustness, failing when applied to unseen datasets or manipulation types.

Purpose of the Study:

• To develop a novel deepfake detection framework with improved cross-dataset and cross-manipulation generalization.
• To enhance the robustness and accuracy of deepfake detection against evolving manipulation techniques.

Main Methods:

• Integration of multiple loss functions: Cross-Entropy Loss, ArcFace loss, and Focal Loss to boost discriminative power.
• Application of the MixStyle technique during training to introduce style diversity and improve model generalization.
• Development of a deepfake detection framework combining these techniques.

Main Results:

• The proposed framework achieved superior detection accuracy in cross-dataset and cross-manipulation tests.
• Demonstrated significant improvements in model robustness and generalizability compared to existing methods.
• Effectively handled complex forgery characteristics and mitigated data imbalance issues.

Conclusions:

• The novel framework effectively addresses the limitations of current deepfake detection methods.
• The combination of advanced loss functions and MixStyle significantly enhances detection performance and robustness.
• This approach offers a promising solution for real-world deepfake detection challenges.