Time to Fake It: Continual Machine Learning Intensifies Unfairness in Deepfake Detection
To this end, we analyze four dimensions of fairness across diverse demographics for detectors trained progressively on a chronological sequence of 11 real-world deepfake generation methods.
ProofPaper ↗
Key points
- Deepfake detectors safeguard against misinformation, rendering their failures as much a societal as a technical concern.
- Tracking fairness metrics over time, we expose non-trivial interplays between performance and fairness that depend on both the choice of CL algorithm and demographic intersections.
- We find accuracy degradation to typically exacerbate unfairness and, conversely, fairness improvements to remain scarce over time.
- Dissecting the CL method families (rehearsal, prompts, regularization), we observe that inequitable forgetting is not explained by underrepresentation alone, but likely entangled with representation learning.
Sources (1)
- [1]Time to Fake It: Continual Machine Learning Intensifies Unfairness in Deepfake DetectionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 02:30 PM
To this end, we analyze four dimensions of fairness across diverse demographics for detectors trained progressively on a chronological sequence of 11 real-world deepfake generation methods.
Deepfake detectors safeguard against misinformation, rendering their failures as much a societal as a technical concern.
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