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A convincing image or video does not prove the claim attached to it. To work out what’s real, verify three things separately: whether the underlying claim is true, what is known about the file’s origin and edits, and whether its caption accurately describes when, where, and why the scene happened. Provenance credentials and AI detectors can help with parts of that job, but neither is a verdict.
What does it mean to verify something online?
Online verification is not a single authenticity test. A video may be genuine but old; an image may be unaltered but staged; a file may have a traceable editing history while its caption makes a false claim. Keep three questions distinct:
- Is the claim true? Look for reliable evidence and independent corroboration.
- What is known about the file? Provenance information may record its source or edits, while a detector may estimate whether a file resembles synthetic media.
- Is the description accurate? Check the date, location, context, and circumstances rather than assuming the caption supplies them correctly.
NIST’s 2024 overview, updated in 2026, surveys approaches including provenance tracking, watermarking, detection, testing, prevention, and auditing; it does not identify one method as sufficient for every task. NIST’s overview of synthetic-content transparency methods is useful context for why verification usually combines evidence rather than relying on one signal.
How can I tell if this image or video is real?
Start with the claim, not with an attempt to guess whether the pixels “look AI.” Preserve what was actually posted, identify the source, and look for evidence that does not depend on the same post or file. The workflow below is useful for images, video, audio, and text claims; adapt the media-specific checks to what you have.
- Record the original post. Save the exact claim wording, account name, date, link, and the original or highest-quality available media. Keep the poster’s assertion intact rather than rewriting it into a stronger or weaker claim.
- Find the primary source. Search for the named person, organization, document, event, or statement. Prefer the original record, such as a full speech, official notice, or source document, when available. Check whether independent secondary coverage confirms the same detail.
- Search for earlier appearances. For images and video, look for prior uses and compare the surrounding details. An earlier publication can reveal that genuine footage is being reused to illustrate a different event.
- Check time, place, and context independently. Compare visible landmarks, weather, signs, language, and other contextual clues with evidence from reliable sources. Check whether the media is cropped, edited, staged, or taken from another occasion.
- Inspect provenance information if present. Note what a credential actually records about the creator, source, or edits, and whether the tool used to verify it conforms to the relevant program. Do not treat a file history as confirmation of its caption.
- Use a detector only as an investigative lead. Confirm that it supports the media type in question. Look for information about its method, training scope, update date, and uncertainty. Record the result as a tool’s assessment, not as proof.
- Report the conclusion with boundaries. Say what is verified, what remains uncertain, and what evidence could change the assessment. Avoid labeling media “AI-generated” merely because it looks unusual or an opaque tool assigns it a high score.
Reuters Institute’s discussion of miscontextualized footage and detector limitations reinforces why date, location, and independent corroboration matter alongside technical analysis. Reuters Institute’s explainer on spotting AI-generated content describes the limits of visual judgment and automated detection.
What do Content Credentials prove?
Content Credentials are tamper-evident, machine-readable labels intended to convey information about media creation or modification. C2PA’s July 2026 resources describe how organizations can choose, verify, and display tools conforming to its program. A credential can therefore provide evidence about a file’s recorded provenance and edits; it does not, by itself, establish that the depicted event happened as claimed or was not staged. C2PA’s resources on Content Credentials and conformance explain the provenance system.
No credential is not evidence that a file is fake. Credentials may be absent because a creator did not attach one, because a tool or platform does not support them, or because metadata was removed as media moved between services. Adoption is incomplete, so provenance is one possible line of evidence rather than a universal authenticity check. Reuters Institute’s 2026 trends report also discusses the broader challenge of miscontextualized media. Reuters Institute’s 2026 trends report provides that context.
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A detector can offer a signal, not a final answer. Its output may be probabilistic or binary, and it is difficult to interpret without knowing the model, training data, target media, and update date. A tool designed for AI-generated images may not recognize face-swapped video; noise can affect audio analysis; unfamiliar subjects, blur, and compression can also make detection harder. Reuters Institute notes examples in which generated or edited images were assessed as likely human or not likely AI-generated. These limitations mean that both a positive and a negative result need corroboration.
- A positive result means the detector flagged a file according to its own method; it does not independently prove who made it or whether the caption is false.
- A negative result does not establish that the media is authentic. The tool may not cover that format, manipulation, or file condition.
- An odd-looking detail is a reason to investigate, not a reliable diagnosis by itself.
There is also a risk in the other direction: real evidence can be dismissed as a deepfake. Treat claims of manipulation with the same demand for evidence as claims of authenticity.
How are newsrooms and fact-checkers using AI in 2026?
Reuters Institute’s report on its March 2026 conference describes AI as both an accelerant for misleading content and a way to help small teams find and classify claims. It reports that Maldita and Full Fact developed systems to detect and classify claims across millions of sentences. Aos Fatos had used an audience-answering chatbot and was developing a live-newsroom fact-checking tool at the time of the report. These examples show ways to support fact-checking workflows; they do not show that automated systems can independently settle complex claims.
In the same report, Reuters Institute says 16% of the 619 claims Aos Fatos fact-checked in 2025 involved AI-generated content, compared with 7% the previous year. Those figures describe one organization’s fact-checking workload, not the prevalence of AI-generated content across the public internet. The Reuters Institute conference report gives the examples and their context.
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Verification is a repeatable information skill, not just a search for software that can label content. UNESCO describes media and information literacy as helping people engage critically with information, navigate online safely, and build trust in information and technology. Its resources include curricula, handbooks, publications, and digital competencies. UNESCO’s media and information literacy resources offer materials for further learning.
UNESCO’s resource page attributes several figures to different populations and measures: it says two-thirds of digital content creators do not systematically fact-check before sharing (UNESCO, 2024); 85% of citizens are worried about online disinformation (UNESCO/IPSOS, 2023); 56% of internet users frequently use social media to stay informed about current events (UNESCO/IPSOS, 2023); and 80% of young people use AI tools and services multiple times a day for education (UNESCO, 2024). These are not a single trend line, and the page’s figures alone do not supply the methodology or country coverage needed to generalize beyond their stated attributions.
When should I ask an expert to verify it?
For a consequential allegation, public-safety issue, health claim, or disputed evidence, do not let one automated tool decide. Seek the primary records, relevant subject-matter expertise, or an experienced fact-checker, and explain what is confirmed separately from what remains uncertain. At a Reuters Institute event in 2026, Full Fact CEO Chris Morris warned that people could lose trust in what they see or hear, while also describing how technology can help address the problem at scale. That is a case for using automation carefully alongside human judgment, not for replacing evidence with a score.
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