A 95% Confidence Score Drops to 60% on Real Evidence—Why Deepfake Detectors Alone Can't Protect Your Case
Dev.to AI
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Machine Learning
Generative AI
Computer Vision
AI Ethics
AI Research
The hidden fragility of deepfake detection models is a wake-up call for anyone building computer vision (CV) pipelines. For developers in the biometrics and facial recognition space, the headline stat is jarring: a detection algorithm boasting 95% accuracy in controlled environments can plummet to 60% when faced with real-world, compressed evidence. In the world of machine learning, we call this "domain shift" or "out-of-distribution" (OOD) data.