Deepfakes of ordinary people

Non-consensual deepfake tools increasingly target everyday people. Verification and refusal are civic craft.

The trap

People still imagine deepfakes as celebrity scandals. The tooling increasingly targets ordinary accounts with small followings, especially women, and the barrier to create has collapsed (Hawkins et al., 2025).

What the evidence shows

Hawkins et al. (2025) found tens of thousands of publicly downloadable deepfake model variants and millions of downloads, with most targeting women and many signaling non-consensual intimate imagery. Flynn et al. (2025) documented perpetrator and victim perspectives on sexualized deepfake abuse, including harms and justifications. The Federal Trade Commission (2022) framed deepfakes among online harms and cautioned against treating detection AI as a complete solution.

What this means for people

Your coworker, classmate, or child does not need fame to be a target. Consent, reporting paths, and “do not generate likeness” norms belong in households and workplaces.

Practice (15 minutes)

Write a team or family rule: no generating identifiable likenesses without explicit consent.
Save two report links (platform abuse form; local guidance you trust).
Run a five-minute drill: how would you verify a shocking clip before resharing?
Tell one person the rule out loud so it exists outside your head.

Reflection

Would you want your likeness in someone else’s “just a joke” generator?

Skeptic check

Download counts are not equal to crimes committed (Hawkins et al., 2025).
Qualitative interviews deepen understanding but do not estimate base rates (Flynn et al., 2025).
Detection tools lag generators (Federal Trade Commission, 2022).

References

Federal Trade Commission. (2022). Combatting online harms through innovation: Federal Trade Commission report to Congress. https://www.ftc.gov/system/files/ftc_gov/pdf/Combatting%20Online%20Harms%20Through%20Innovation%3B%20Federal%20Trade%20Commission%20Report%20to%20Congress.pdf

Flynn, A., Powell, A., Eaton, A., & Scott, A. J. (2025). Sexualized deepfake abuse: Perpetrator and victim perspectives on the motivations and forms of non-consensually created and shared sexualized deepfake imagery. Journal of Interpersonal Violence. Advance online publication. https://doi.org/10.1177/08862605251368834

Hawkins, W., Mittelstadt, B., & Russell, C. (2025). Deepfakes on demand: The rise of accessible non-consensual deepfake image generators. In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency (pp. 1602-1614). Association for Computing Machinery. https://doi.org/10.1145/3715275.3732107