arXiv:2606.09881v1 Announce Type: new Abstract: Deepfake detectors show large performance gaps across demographic groups. Existing fairness approaches require demographic labels, retraining, or sacrifice accuracy. We introduce Face-Fairness (FF), a plug-and-play framework for bias mitigation. Our primary contribution, Face-Feature Tuning (FFT), is the first demographic label-free fairness method demonstrated for deepfake detection: a lightweight calibrator that performs a logit remapping conditioned on frozen face embeddings. We complement FFT with two variants: FF-Max, which maximizes worst-g
The proliferation of deepfake technology has exposed significant performance disparities in detection across demographic groups, necessitating immediate solutions as AI adoption scales.
Addressing bias in deepfake detection is crucial for maintaining trust in digital media, ensuring equitable application of AI security measures, and preventing the weaponization of identity.
The introduction of demographic label-free bias mitigation for deepfake detection provides a more scalable and less data-intensive approach to fairness, improving the integrity and applicability of these security tools.
- · AI ethics and fairness researchers
- · Digital forensics companies
- · Social media platforms
- · Individuals in underrepresented demographic groups
- · Malicious actors using deepfakes for disinformation
- · Companies with biased deepfake detection systems
Deepfake detection systems become more reliable and fair across diverse populations, enhancing their utility in real-world scenarios.
Reduced demographic disparities in deepfake detection could lead to a broader adoption of AI-powered verification and security tools, fostering greater trust in digital interactions.
This ethical advancement in AI could set a precedent for developing more equitable AI systems across other sensitive applications, potentially accelerating a 'fair AI' arms race.
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