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Research

AI detection statistics, 2026.

The state of synthetic media is hard to track because the categories keep shifting. This page is the curated set of figures we use ourselves when writing about detection — volume, fraud impact, accuracy, adoption — with brief context for each.

Last reviewed 2026-05-10 · Reviewed quarterly

Volume of AI-generated content

Industry trackers consistently estimate that the share of AI-generated images on social platforms has grown from a fraction of a percent in 2022 to a measurable double-digit share of new uploads on the largest platforms by late 2025. The exact figure depends on which platform, which content type, and how strict the detection threshold is — but the trend line is unambiguous.

AI-generated text is harder to measure but almost certainly higher. Industry surveys of marketing and customer-support content suggest majority-AI-assisted writing in those categories is now the norm rather than the exception.

Deepfake-driven fraud

Deepfake-enabled financial fraud — voice-clone executive impersonation, face-swap video calls used to authorize transfers, AI-generated identity documents — has shifted from research curiosity to a routinely-cited category in financial-crime reporting. Multiple regulator-published advisories now treat synthetic-media fraud as a standard threat type requiring specific controls.

Direct dollar-loss figures vary widely depending on definition and source. The order of magnitude is in the billions of dollars per year globally, with recorded incidents skewed toward business-email compromise, romance scams, and instant-payment social engineering.

Detection accuracy in the field

Public benchmark scores for AI detection consistently overstate in-the-wild performance. A detector reporting 95% accuracy on its training-distribution benchmark may drop to 60–80% on real social-media content because of compression, recompression, and adversarial tuning.

The practical takeaway: combine detection with provenance and source verification. See why AI detectors are not 100% accurate.

Provenance adoption

C2PA-style content credentials — see the glossary entry — are now supported by major camera manufacturers, several image-editing vendors, and a growing share of model providers. Adoption on user-facing surfaces (social-media platforms displaying credentials in the UI) is lagging the back-end implementation by one to two years.

Most-reported scam categories using AI

  1. 1. AI-generated profile photos in romance and recruiter scams.
  2. 2. Voice-clone panic calls to relatives requesting urgent money.
  3. 3. Fabricated payment-proof screenshots in marketplace deals.
  4. 4. Deepfake CEO video on chat apps authorizing wire transfers.
  5. 5. AI-narrated “side hustle” explainers driving paid courses or token pumps.

Methodology and sources

Figures on this page are aggregated from publicly-reported industry surveys, regulator advisories, peer-reviewed evaluation papers on detection benchmarks, and our own platform observations. We deliberately avoid quoting precise percentages where the underlying ranges are wide, because doing so creates false precision. When we cite a specific number in our blog posts, we link the source on that post.

If you want to cite this page, please link to the canonical URL — the figures are reviewed quarterly and pinned numbers go stale fast.