You cannot reliably judge an online claim by whether its image or video looks real. Before sharing or acting on a consequential post, check who published it, find the original material and its context, assess the claim and its purpose, and seek confirmation from independent reliable sources. Treat AI labels, provenance records, and detection results as clues—not proof that a claim is true or false.
How to check a suspicious post before sharing it
Use the same critical checks for text, images, audio, and video. The UK Government’s 2025 guidance on deepfakes and media literacy identifies source, content, plausibility, and purpose as useful factors when assessing online misinformation, including AI-generated disinformation.
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- Pause when the post demands urgency. Be especially cautious if it asks you to send money, reveal credentials, or take immediate action. Urgency is a reason to verify first, not evidence that a post is false.
- Identify the source. Find the original publisher or speaker rather than relying on a repost, cropped screenshot, or forwarded clip. Check whether the source is identifiable and whether it provides context and supporting evidence.
- Find the original material and context. Look for the complete post or clip, its date and location, and any evidence offered for the claim. A genuine image or recording can still be old, edited, or presented out of context.
- Assess plausibility and purpose. Ask whether the claim fits what is known and what the post appears designed to make you believe or do. A dramatic presentation is not a substitute for evidence.
- Corroborate consequential claims. Look for confirmation from independent, reliable reporting or authoritative records before acting. Do not let a “looks real” or “looks fake” judgment decide the issue.
The UK Government describes media literacy as useful for improving people’s ability to critically access, analyse, evaluate, and communicate messages in different forms. Its guidance applies these established media-literacy checks to AI-generated disinformation, rather than making visual inspection the sole test.
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Technical systems can add information about synthetic media, but each answers a limited question. NIST’s Reducing Risks Posed by Synthetic Content: An Overview of Technical Approaches to Digital Content Transparency (NIST AI 100-4, published November 20, 2024; page updated April 8, 2026) reviews approaches including content authentication and provenance, labeling and watermarking, detection, testing, and auditing.
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| Approach | What it can indicate | What it does not establish by itself |
|---|---|---|
| Provenance or authentication records | Information about a piece of content’s origin or how it has changed, when records are available. | That the claim shown or described is true, or that every step in the content’s history is recorded. |
| AI labels or watermarks | A signal that content has been identified or disclosed as synthetic. | That the labeled content is false. Likewise, no visible label does not prove that content was made by a person. |
| Synthetic-content detection | An assessment of whether content has characteristics associated with synthetic media. | A universal or definitive verdict. NIST’s overview does not establish one detector that identifies every fake or a single consumer verification workflow. |
These approaches provide different kinds of evidence; they are not interchangeable truth tests. Human-made content can be false, while synthetic content can convey accurate information. Consider any label, provenance record, or detection result alongside the source, context, and independent corroboration.
What individuals, platforms, and educators can do
Reducing risk is not only a matter of spotting generated media. It also depends on the habits and systems that help people evaluate information.
- As a reader: slow down, trace claims to their source, check context, and verify important claims independently before sharing or acting.
- As a publisher or platform: technical transparency measures such as provenance, labeling, detection, and auditing can contribute useful signals. Their presence does not replace editorial verification or establish a claim’s truth.
- As an educator or community organization: teach media and AI literacy together, including how to assess source reliability and evidence in material involving generative AI.
- Within an organization: as a practical approach, assign responsibility for checking high-impact claims, retain relevant original material and context, correct errors transparently, and teach staff how to verify and report suspected manipulation. This is an application of broader literacy and transparency principles, not a formal protocol prescribed by NIST or UNESCO.
UNESCO’s February 2024 policy-brief summary says media and information literacy can support ethical use of synthetic media and recommends embedding AI literacy in media and information literacy education. UNESCO’s Recommendation on the Ethics of Artificial Intelligence, adopted November 23, 2021, calls for investment in these skills to strengthen critical thinking and help address misinformation and disinformation.
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The approaches above are complementary, but the available guidance does not provide comparable performance figures for ranking them or a measured success rate for how much they reduce an individual reader’s risk. Avoid promises that a detection tool will reliably catch every fake, or that following a checklist guarantees a correct judgment. Technical features and platform processes can change, and local reporting routes and policies vary.
UNESCO’s Media and Information Literacy facts and figures page states that two-thirds of digital content creators do not systematically fact-check information before sharing it online, attributing the figure to its 2024 work. That figure concerns digital content creators generally; it is not a measure of AI-generated misinformation or of any verification method’s effectiveness.
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