The Deepfakes We Missed: We Built Detectors for a Threat That Didn't Arrive
Nearly a decade of Machine Learning (ML) research on deepfake detection has been organized around a threat model inherited from 2017--2019, revolving around face-swap and talking-head manipulation of public figures, motivated by concerns about large-scale misinformation and video-evidence fraud. This position paper argues that the threat the field prepared for did not arrive, and the threats that did arrive are substantially different. An accounting of deepfake incidents in 2022--2026 shows that the dominant observed harms are peer-generated Non-Consensual Intimate Imagery (NCII), voice-clone scam calls targeting families and finance workers, and emotional-manipulation fraud. The predicte...
| Authors | Shaina Raza |
|---|---|
| Primary category | cs.CR |
| Published | 12 May 2026 |
| Last updated at source | 12 May 2026 |
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