Face Forgery Detection With CLIP-Enhanced Multi-Encoder Distillation.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Face forgery technology is rapidly advancing, posing significant threats to digital security and authenticity.
• Existing face forgery detection methods struggle with comprehensive feature extraction and adaptability to complex scenarios.
• Multimodal models offer new avenues for forgery detection, but current approaches often oversimplify prompt engineering.

Purpose of the Study:

• To propose an advanced face forgery detection method addressing limitations in current techniques.
• To leverage multimodal models, specifically CLIP, for enhanced forgery detection capabilities.
• To improve model adaptability and feature extraction comprehensiveness in detecting sophisticated fake faces.

Main Methods:

• Developed a method based on multi-encoder fusion and cross-modal knowledge distillation.
• Fused prior knowledge from CLIP (text and image encoders) with a specialized forgery detection model (Deepfake-V2-Model) as a teacher model.
• Employed alignment distillation to transfer visual abnormal patterns and semantic features from the teacher to a student model.

Main Results:

• The proposed method effectively integrates CLIP's representational power and generalization abilities.
• The student model successfully acquired forgery detection knowledge through aligned representations.
• Experimental results demonstrate significant performance improvements in face forgery detection.

Conclusions:

• The multi-encoder fusion and cross-modal knowledge distillation approach enhances face forgery detection.
• This method overcomes limitations of existing techniques by improving feature extraction and model adaptability.
• The study highlights the potential of combining large multimodal models with specialized knowledge for improved security applications.