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A Google-led study found that AI-generated images rose sharply in fact-checked visual misinformation, especially after early 2023. It did not find that AI is the top source of misinformation across the internet. The often-cited figure of roughly 80% refers to recent misinformation claims involving media such as images, video or audio—not to AI-generated content.
What the Google-led study examined
The study, A Large-Scale Survey and Dataset of Media-Based Misinformation In-The-Wild, is known as AMMeBa. Led by Google researcher Nicholas Dufour, it was a collaboration involving researchers and fact-checking organizations including Factly Media & Research, Full Fact, the Duke University Reporters’ Lab and Maldita.es. The paper is available as a 2024 preprint on arXiv.
Researchers assembled and hand-annotated media associated with 135,838 public fact checks. Much of the material was found through ClaimReview, a structured format that fact-checking publishers can use to describe claims and their ratings. The collection reaches back to 1995, but its historical coverage is uneven and most observations come from after ClaimReview’s introduction in 2016. The paper describes a survey of media connected to fact-checked misinformation—not a census or random sample of everything false or misleading online.
The study’s data collection ended in November 2023. Its trends describe the fact-checking material collected up to that point, not a live estimate for 2026. The paper’s status as an arXiv preprint is stated on its record; readers should check that page for current publication details.
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What the 80% and 30% figures mean
The statistics refer to different groups and should not be combined into a claim that AI produces most misinformation.
- About 80%: In the recent sample summarized in the Google-hosted dataset description, roughly 80% of misinformation claims involved some form of media, such as an image, video or audio. That says nothing by itself about whether the media was AI-generated.
- Nearly 30%: By the end of data collection, in November 2023, AI-generated content accounted for nearly 30% of fact-checked image-content manipulations, according to the same dataset description. This is not 30% of all misinformation online; it concerns fact-checked image-content manipulations in the collected material.
Google’s News Initiative training likewise summarizes the media finding as about 80% of misinformation involving photos or video. The essential distinction remains: media involvement is not AI involvement.
AI imagery rose quickly, but did not erase older tactics
In the dataset, AI-generated and AI-manipulated images were negligible for much of the historical period, then increased sharply in spring 2023. That timing coincided with the wider availability of consumer image generators and viral fabricated images, including the picture of Pope Francis in a large white coat. The rise is a change in the composition of the study’s fact-checked sample; it does not measure the total volume of AI misinformation circulating online.
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Nor did synthetic media replace the older, simpler practice of misleading people with genuine material. The dataset describes context manipulation—using an authentic image or other media with a false caption, date, location or interpretation—as a historically dominant pattern. An old photograph passed off as current, or an image from one country attributed to another, can mislead without changing a single pixel.
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Video also became more prominent in later fact-checked claims. The paper and Google training materials give figures with different denominators and time windows, so they should not be collapsed into a single percentage. The reliable takeaway is the direction of the trend: video appeared increasingly often in the later sample, not that one percentage applies to every platform or all misinformation.
Why the headline goes beyond the evidence
“AI is the top source of misinformation online” is broader than what AMMeBa measured. The study assessed media attached to claims that professional fact checkers had selected and published. It did not compare AI with every other source of falsehood across text, audio, video, private conversations and offline channels, nor rank the causes of all online misinformation.
Fact-checking data are shaped by what can be seen and investigated. Fact checkers cannot review every suspicious post, and ClaimReview depends on publishers choosing to use the markup. Highly visible or unusual material may be more likely to attract a fact check, while posts in private groups, ephemeral content, material in less-covered languages and claims never selected for review may be missing. Lead author Dufour discussed limits in fact-checking capacity and selection effects in reporting by 404 Media.
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Classification is another boundary. A real photograph paired with an AI-written caption, a genuine image edited with generative fill, or authentic footage with a synthetic voiceover sits between fully authentic and fully generated media. The study’s categories should not be read as a complete accounting of every possible use of AI.
Why the growth still matters
Even without proving that AI is the biggest source of misinformation overall, the sharp increase in fact-checked synthetic imagery is significant. Image generators can make it easier to produce and revise plausible-looking scenes, including ones tailored to current events. That possibility raises the workload for people trying to establish where an image came from and what it depicts. The AMMeBa sample, however, does not quantify the internet-wide scale or persuasive effect of those advantages.
Seeing an AI-generated image also does not automatically mean a viewer will believe it. Reactions can depend on plausibility, prior beliefs, the accompanying caption, the source and whether the viewer sees a warning or provenance information. A separate 2025 PNAS Nexus study used two preregistered survey experiments with 7,579 Americans to examine labels on misleading AI images; its results address labeling and viewer responses, not the prevalence measured by AMMeBa. See the study.
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No single visual oddity, detector result or missing label can settle whether media is authentic. Check the claim and the media’s history together.
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- Search for earlier appearances. Use Google Lens or TinEye to look for matches and visually similar images. An earlier result may reveal the original date, caption or location. No match is not proof of authenticity: new, cropped or rarely indexed images may not appear.
- Find the earliest available source. Reposts and screenshots can obscure who first published a file. Look for the original account, publication or photographer, and check whether reputable reporting independently supports the claim.
- Check time and place, not just pixels. A genuine photograph can be old, taken elsewhere or used with a false caption. Search distinctive phrases from the post and compare the asserted event with reliable reporting or official statements.
- For video, search key frames. A clip may be old footage reused to depict a new event. Extract or capture distinctive frames and reverse-search them; examine the full clip, not only a short crop, and look for context removed by edits.
- Treat visual clues as prompts, not proof. Strange text, fingers, reflections, shadows, logos or perspective can justify closer checking, but image generation and editing tools change, and genuine images can also contain odd-looking details.
- Check provenance when available. AI disclosures or Content Credentials can offer useful information about a file’s origin or editing history. Their absence does not establish that a file is human-made, and provenance cannot prove that the caption or claim is true.
For a fact-check search, Google’s Fact Check Explorer training explains how to use the tool. Journalists and advanced users can also consult the InVID-WeVerify verification plugin for workflows that include key-frame extraction and contextual checks.
What provenance and labels can—and cannot—tell you
Content Credentials are a provenance approach built around the C2PA specification. Where credentials are present and verifiable, they can help describe a file’s creation or editing history. They do not certify that the scene depicts the event claimed in a post. A correctly documented photograph can still be paired with a lie; a file without credentials is not automatically fake.
The same distinction applies to platform labels and creator disclosures: they can inform an assessment, but they are not a substitute for checking source, date, context and independent corroboration. For the structured fact-check markup used in much of AMMeBa’s collection, see the ClaimReview Project.
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The accurate conclusion is narrower than the provocative headline: AI-generated imagery rapidly became a major component of fact-checked visual misinformation by late 2023, while misleading uses of genuine media remained important. The study’s 80% figure is about claims involving media, not AI. AMMeBa offers a large view of material fact checkers documented, not proof that AI is the leading source of misinformation across the internet.
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