EFIMD-Net: Enhanced Feature Interaction and Multi-Domain Fusion Deep Forgery Detection Network.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Digital Forensics

Background:

• Deepfake technology poses significant risks, necessitating advanced detection methods.
• Existing deepfake detection networks struggle with robustness, generalization, and single-domain feature extraction.
• Evolving deepfake algorithms and diverse datasets challenge current detection capabilities.

Purpose of the Study:

• To propose EFIMD-Net, a deepfake detection network designed to overcome limitations of existing methods.
• To enhance feature interaction and integrate multi-domain features for improved detection performance.
• To validate the effectiveness and generalization of the proposed network on various datasets.

Main Methods:

• Developed EFIMD-Net integrating a Cross-feature Interaction Enhancement (CFIE) module for adaptive spatial-frequency feature fusion.
• Employed a channel attention mechanism within CFIE to fuse macro-semantic and high-frequency artifact information.
• Introduced an Enhanced Multi-scale Feature Fusion (EMFF) module for adaptive integration of multi-scale features.

Main Results:

• EFIMD-Net demonstrated comparable or superior Area Under the Curve (AUC) performance against the Xception baseline on multiple datasets.
• Ablation studies confirmed the effectiveness of the CFIE and EMFF modules.
• EFIMD-Net significantly outperformed the Locate and Verify baseline, showing a 9% AUC increase on CelebDF-v1 and a 7% increase on CelebDF-v2.

Conclusions:

• EFIMD-Net exhibits strong effectiveness and generalization capabilities for deepfake detection.
• The integration of spatial and frequency domain features significantly enhances detection performance.
• Future work may address potential limitations in real-time processing efficiency.