What are the odds an AI-generated intimate deepfake of you will be created or shared without consent in your lifetime?
Evidence quality 4.13/5
Eight-dimension review score against the quality rubric . Each dimension scored 1–5.
- D1 Source grounding
- 4/5
- D2 Source authority
- 5/5
- D3 Arithmetic
- 3/5
- D4 Uncertainty
- 4/5
- D5 Scope
- 4/5
- D6 Prose
- 4/5
- D7 Perception honesty
- 4/5
- D8 Caveat completeness
- 5/5
Lifetime probability · lifetime, US adult
1 in 25
4.0% lifetime chance
Most people underestimate this.
range 1 in 50 to 1 in 10
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≈ As likely as
Perceived
Public discourse about non-consensual intimate deepfakes is dominated by a handful of celebrity cases — Taylor Swift in January 2024, women in the US Congress later that year, K-pop idols across multiple incidents. The mental model that emerges is "this happens to famous women on the internet," which lets ordinary adults file the risk under things-that-happen-to-other- people. The cost trajectory of the underlying tools does not support that framing: open-source models that required a research-grade GPU in 2019 now run on consumer phones, and a usable face-swap on a single photograph takes minutes. Asked directly, most adults significantly underestimate the population prevalence of personal victimization, which a peer-reviewed survey of ~16,000 respondents across 10 countries put at 2.2% as of 2023 — in absolute terms, several million people, drawn overwhelmingly from a single half of the population.
Rough estimate: Most adults file this under 'happens to celebrities' rather than a personal risk
Source: editorial intuition, not polled
Actual
~2 in 100 (ever, across 10 countries 2023)
adults aged 16+ across 10 countries (Australia, Belgium, Denmark, France, Italy, Netherlands, Mexico, South Korea, UK, US), Umbach et al. CHI 2024
Show derivation
The 2.2% figure from Umbach et al. (2024) is a contemporaneous "ever experienced" prevalence measured in 2023, when consumer-grade deepfake tools had been broadly accessible for roughly five years. Translating it into a remaining-lifetime probability for a US adult requires two adjustments in opposite directions. First, the headline understates true victimization because (a) many victims do not know intimate deepfakes of them exist — the imagery is shared in closed channels or hosted on dedicated sites the subject never visits, and (b) self-report surveys chronically under-capture sexual-violence categories due to recall and disclosure barriers, with Eaton et al.'s (2017) US-only NCII survey finding 8% lifetime prevalence on a broader definition that includes real (non-AI) intimate imagery. Second, the technology curve is steep: the Home Security Heroes census found a 550% increase in detected deepfake videos from 2019 to 2023, almost entirely pornographic and targeting women. A reasonable lifetime estimate combining the peer-reviewed contemporary base rate with a moderate growth assumption over a 59-year horizon lands near 4% — roughly double the 2023 prevalence, well below Eaton's broader-definition 8% which captured fifteen years of pre-AI image-based abuse. The uncertainty band is wide (2%-10%) and skews upward because the technology is still in rapid diffusion.
Caveats: Three caveats matter more than the headline number. First, the gender asymmetry …
Three caveats matter more than the headline number. First, the gender asymmetry is severe enough that the population-average figure misleads: the 4% lifetime estimate is roughly a weighted average of a female rate near 7-8% and a male rate well under 2%. Every census of deepfake-video supply finds the pornographic share targets women essentially exclusively. Reporting only the population average would understate the risk for women and overstate it for men, which is why the personal-factor multipliers are load-bearing rather than decorative. Second, the survey data is almost certainly conservative. Many victims of synthetic intimate imagery never learn the imagery exists — it can be generated, shared, and consumed entirely outside the subject's social graph. The 2.2% contemporary figure measures self-aware victimization; the true rate is necessarily higher and unknowable by self-report. This is structurally different from cyberbullying or harassment, where the victim is by definition aware of the event. Third, no comparable peer-reviewed time series exists. Umbach et al. (2024) is the first multi-country population survey on this specific category. Deeptrace 2019, Home Security Heroes 2023, and the Internet Watch Foundation reports measure supply (videos online) rather than demand-side victim prevalence. The uncertainty band reflects both measurement gaps and the steepness of the technology adoption curve — any number computed from 2023 data may be substantially low by 2030.
Risks at similar odds
Other risks with roughly the same likelihood — useful for calibration.
Kids & explicit content
What are the odds of a child encountering explicit or violent content online before age 13?
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The peer-reviewed anchor is Umbach, Henry, Beard and Berryessa’s 2024 paper in the CHI proceedings, which surveyed roughly 16,000 adults across ten countries — Australia, Belgium, Denmark, France, Italy, Netherlands, Mexico, South Korea, the UK and the US — and reported that 2.2% had personally experienced non-consensual synthetic intimate imagery, with 1.8% reporting having perpetrated it. The fielding year was 2023, when consumer-grade deepfake tools had been broadly accessible for roughly five years. Read as a pure lifetime-to-date number, 2.2% is small. Read as the prevalence of a category that effectively did not exist a decade earlier, during a period when detected deepfake-video supply was growing at triple-digit annual rates, it is a base rate in transit rather than a base rate at rest.
The gender asymmetry dominates everything else. Sensity AI’s original 2019 census of 14,678 deepfake videos found that 96% were pornographic and that every pornographic deepfake observed depicted a female subject — the authors flatly stated that deepfake pornography “exclusively targets and harms women.” Home Security Heroes’ 2023 follow-up census, on a sample nearly seven times larger (95,820 videos), put the pornographic share at 98% and the female-target share at 99%. The Markup’s 2024 analysis of US Congress found that one in six congresswomen had been targeted with sexually explicit AI imagery, against essentially zero of their male colleagues. The 4% lifetime estimate here is a population average; for women the realised rate sits several multiples higher, and for men materially lower.
The harm pathway is documented and well-replicated. Eaton, McGlynn and others (Psychology of Violence, 2020) frame non-consensual intimate imagery — synthetic or otherwise — as a form of sexual violence, not as a property or reputation tort, because the documented outcomes track those of contact sexual offences: elevated depression and anxiety, withdrawal from public-facing professional roles, suicidality in a non-trivial subset. UN Special Rapporteur reporting in 2026 described the chilling-effect dynamic for women in public life as “virtual rape,” language that may sound rhetorical but is operationally accurate about the psychological burden victims report. Self-censorship is the most common downstream behaviour: surveys of women journalists and elected officials repeatedly find 40%+ saying they have reduced public output to avoid abuse, and synthetic imagery sits at the severe end of that abuse distribution.
What is genuinely uncertain is the lifetime ceiling. Eaton’s 2017 US survey put broader non-consensual intimate imagery — the category that includes real photographs shared without consent — at 8% lifetime prevalence, which is the natural ceiling for synthetic-imagery victimization unless the AI-generated subcategory eventually exceeds the pre-existing real-imagery category in scale. There are structural reasons to expect partial substitution rather than pure addition (the synthetic version requires no compromising photograph to exist in the first place, which widens the victim pool considerably) and other reasons to expect addition (most victims of real-imagery NCII had a prior intimate relationship with the perpetrator, while synthetic imagery has no such gating). The 4% central estimate splits the difference; the 2-10% uncertainty band acknowledges that the trajectory matters as much as the present level.
The third structural caveat is detection. A victim of cyberbullying or direct harassment knows by definition that the event occurred. A victim of non-consensual synthetic imagery may never know — the imagery can be generated, traded in closed channels, and consumed entirely outside the subject’s social graph. Self-report surveys like Umbach et al. measure self-aware victimization. True victimization is necessarily higher, by an unknown but probably non-trivial multiple. This is the single largest reason to treat the 2.2% headline as a floor rather than a point estimate, and to flag the entry as underrated despite the recent surge in press attention to the topic.
Claim ledger
Every number below is what each source reported, with the verbatim quote we relied on and how we arrived at our figure. Click any link to verify directly.
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[1] Umbach, Henry, Beard & Berryessa — Proceedings of CHI 2024 (arXiv preprint) — Non-Consensual Synthetic Intimate Imagery: Prevalence, Attitudes, and Knowledge in 10 Countries
Non-Consensual Synthetic Intimate Imagery: Prevalence, Attitudes, and Knowledge in 10 Countries- Statistic
2.2% of all respondents indicated personal victimization with non-consensual synthetic intimate imagery; 1.8% indicated perpetration; survey of >16,000 respondents across 10 countries (Australia, Belgium, Denmark, France, Italy, Netherlands, Mexico, South Korea, UK, US)- Excerpt
“"2.2% of all respondents indicated personal victimization, and 1.8% all of respondents indicated perpetration behaviors regarding deepfake pornography." ”
- Source data from
- 2024-02-02
- Accessed
- 2026-05-28 · archived copy
- Calculation
- Umbach et al. (2024) is the first peer-reviewed multi-country population survey specifically measuring synthetic (AI-generated) intimate-imagery victimization rather than the broader image-based-abuse category. The paper was accepted to CHI 2024 (the flagship ACM human-computer interaction conference). Sample is >16,000 respondents across the 10 listed countries; the 2.2% figure is the pooled personal-victimization rate across all respondents. This is the headline native rate. The fielding year is 2023, so the figure is best interpreted as "ever experienced as of 2023" — a contemporaneous lifetime-to-date prevalence with the technology only ~5 years into mainstream availability. The lifetime extrapolation to ~4% combines this base with a moderate forward-growth assumption (Home Security Heroes census shows 550% growth 2019-2023), capped well below the 8% lifetime rate Eaton et al. found for the broader NCII category that includes real imagery.
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[2] Eaton, Jacobs & Ruvalcaba — Cyber Civil Rights Initiative — 2017 Nationwide Online Study of Nonconsensual Porn Victimization and Perpetration
2017 Nationwide Online Study of Nonconsensual Porn Victimization and Perpetration- Statistic
1 in 12 (8.0%) of US adult respondents reported lifetime victimization of nonconsensual pornography; 1 in 20 (5%) reported perpetration; women's victimization rate was higher than men's; n=3,044 US adults- Excerpt
“"Among participants (54% women), 1 in 12 reported at least one instance of nonconsensual pornography victimization in their lifetime, and 1 in 20 reported perpetration of nonconsensual pornography. Women reported higher rates of victimization and lower rates of perpetration than men." ”
- Source data from
- 2017-06-12
- Accessed
- 2026-05-28 · archived copy
- Calculation
- The Eaton/CCRI 2017 study is the most-cited US adult NCII prevalence figure and was published as a peer-reviewed follow-up in Psychology of Violence (Ruvalcaba & Eaton, 2019). It covers all non-consensual intimate imagery — overwhelmingly authentic photographs taken consensually and later distributed without consent — not just AI-generated deepfakes, which were not yet a measurable category in 2016 when the survey was fielded. The 8% lifetime figure is included here as the broader-category ceiling: any plausible deepfake-specific lifetime rate should sit at or below this number, because synthetic imagery is one mechanism within a larger problem that pre-dates the AI tools. The CCRI sample (3,044 US adults via online panel) has the usual online-panel skew but is the most authoritative US-specific headline number available. Used to set the upper bound on the uncertainty interval, not the central estimate.
- Independence
- Eaton et al. used a US-only online probability panel through CCRI, entirely separate from the Umbach et al. 10-country fielding. The broader definition (real + synthetic NCII) and the seven-year gap make the two figures complementary, not redundant: Umbach captures the synthetic share of a recent year; Eaton captures lifetime prevalence of the broader category before consumer deepfake tools existed.
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[3] Silicon Republic — coverage of Deeptrace (Sensity) State of Deepfakes 2019 — 96pc of deepfakes online are pornographic in nature (Deeptrace report coverage)
96pc of deepfakes online are pornographic in nature (Deeptrace report coverage)- Statistic
Deeptrace 2019 census found 14,678 deepfake videos online; 96% were non-consensual pornography; every observed pornographic deepfake portrayed a female subject- Excerpt
“"Deepfake pornography is a phenomenon that exclusively targets and harms women." ”
- Source data from
- 2019-10-08
- Accessed
- 2026-05-28 · archived copy
- Calculation
- The Deeptrace (later renamed Sensity AI) 2019 report is the earliest systematic census of deepfake videos online and established the gender asymmetry that has held in every subsequent measurement: pornographic deepfakes overwhelmingly target women. Used here purely to source the gender multiplier — the headline native rate is from Umbach, the severity asymmetry is from Deeptrace. The original report PDF is published on Sensity's site; this Silicon Republic coverage is used because the primary host returns 403 to automated retrieval. The 96% pornographic / ~100% female-target finding has been replicated by Home Security Heroes (2023) on a much larger 95,820-video sample with 99% female-target.
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[4] Home Security Heroes — 2023 State of Deepfakes: Realities, Threats, and Impact
2023 State of Deepfakes: Realities, Threats, and Impact- Statistic
95,820 deepfake videos identified online in 2023 (a 550% increase from 2019); 98% of deepfake videos online are pornographic; 99% of deepfake pornography targets women- Excerpt
“"Deepfake porn makes up 98 percent of all deepfake videos online, with 99 percent of them targeting women." ”
- Source data from
- 2023-09-01
- Accessed
- 2026-05-28 · archived copy
- Calculation
- The 2023 census is included for trajectory rather than base-rate: the 550% growth from 2019 to 2023 (14,678 → 95,820 detected videos) is the diffusion signal that drives the upward skew on the uncertainty band. Home Security Heroes is a commercial security publication rather than peer-reviewed academia, but its census methodology mirrors Deeptrace's 2019 work and the corroborating 99% female-target figure aligns with both the academic literature and the Sensity follow-ups. Used to justify treating the 2.2% contemporary prevalence as a lower bound on true lifetime exposure given the rapid capability and accessibility growth.