FairForensics: mitigating attribute bias in deepfake detection by integrating texture and attribute features.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Deepfake technology synthesizes realistic face-swapping videos, posing risks like misinformation and identity fraud.
• Existing deepfake detection methods often exhibit bias across attributes like gender and race, limiting reliability.
• Fairness in AI detection systems is critical for equitable and trustworthy applications.

Purpose of the Study:

• To introduce FairForensics, a novel method for face forgery detection that addresses fairness concerns.
• To enhance deepfake detection accuracy while mitigating attribute bias.
• To develop a reliable system for identifying manipulated videos across diverse demographics.

Main Methods:

• FairForensics extracts attribute and texture features using dedicated modules (fairtexture and fairattribute).
• It compares facial attributes across video frames to identify temporal inconsistencies characteristic of forgeries.
• A spatial-temporal feature aggregator integrates texture and attribute features using attention mechanisms for fair representation.

Main Results:

• The proposed approach significantly improves face forgery detection accuracy.
• FairForensics effectively reduces detection disparities across different face attributes, mitigating bias.
• The method demonstrates enhanced reliability in identifying deepfakes across diverse demographic groups.

Conclusions:

• FairForensics offers a more accurate and equitable solution for deepfake detection.
• This work contributes to developing trustworthy AI systems by addressing fairness in face forgery detection.
• The findings provide a promising approach to combat the societal risks associated with deepfake technology.