Look for visual inconsistencies, check any available provenance information, and verify the post’s claim independently. None of these checks alone proves an image is AI-generated—or authentic. A convincing synthetic image may show no obvious flaws, while a real photograph can contain strange-looking details.
Use a three-part check: image, provenance, and context
When a social post makes a striking claim, separate three questions: Do details in the image look unusual? Is there a supported record of where the file came from or how it was made? Does independent evidence support the caption? These checks answer different questions. Visual inspection can raise doubts, provenance can document aspects of a file’s origin, and verification can test the post’s claim.
- Pause and identify the claim. Note exactly what the post says happened, and whether it presents the image as a photograph, illustration, or AI creation. Be especially cautious before sharing an emotionally charged or shocking post.
- Inspect the image for inconsistencies. Zoom in on details that should fit together, such as anatomy, object boundaries, repeated patterns or text, perspective, shadows, highlights, and reflections. Treat anything odd as a reason to investigate, not a verdict.
- Check the post’s context. Look at who posted it, when it appeared, the location and caption, and whether reliable independent sources show the same event. Search for earlier appearances or other context when available.
- Look for disclosure or provenance. Check for a platform label or Content Credentials. If you can obtain the original image file, use a verifier that explains which signals and generators it supports. Preserve the original if possible; editing or sharing may remove metadata.
- Interpret the result narrowly. A detected signal is evidence of a supported association, not proof that the caption is true or that the image has not been altered. No signal found is inconclusive.
- Escalate high-stakes cases. Do not accuse someone or publish a consequential claim based only on appearance or an automated score. Seek independent corroboration or qualified media-forensics review.
What visual clues can—and cannot—tell you
Commonly noticed clues can be grouped into anatomy, style, function, physics, and sociocultural context. In practice, look for details that fail to agree with the rest of the scene:
- Anatomy and structure: body parts or object boundaries that seem inconsistent.
- Style and repeated detail: mismatched features, patterns, or text that do not hold together on closer inspection.
- Function and interaction: objects that appear not to interact plausibly with people or their surroundings.
- Physics: lighting, shadows, highlights, reflections, or perspective that do not cohere.
- Sociocultural context: signs, clothing, objects, or other details that do not fit the claimed time or place.
These are prompts to check, not a universal detection test. The 2024 guide How to Distinguish AI-Generated Images from Authentic Photographs groups artifacts into those categories and warns that artifacts are not always visible. Its study set—138 AI-generated images, nine images curated from social media, and 42 real photographs—is the material examined by the authors, not a measure of universal detector accuracy. Pixels alone may not reveal an image’s provenance, and unusual details can occur in genuine photographs.
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How provenance checks work
Provenance systems look for information associated with a file; they do not determine whether the story attached to it is true. The result depends on which system produced or signed the information, whether the checker supports it, and whether the relevant information survived sharing or editing.
| Approach | What it can show | Important limit |
|---|---|---|
| Content Credentials (C2PA) | A signed provenance record may carry assertions about an asset’s origin, modifications, and AI use. | It checks whether provenance information is well-formed, valid, associated with the asset, and signed by an issuer that may be checked against a trust list; it does not judge whether the assertions are true. Metadata can be lost during editing or sharing. |
| Model-specific watermark | A supported verifier may detect an embedded signal associated with a particular system. | Coverage depends on the vendor, model, and verifier. A result does not establish how the file was used or whether it was modified afterward. |
| Visual inspection or an AI detector | Inspection can surface visual anomalies; a detector may report evidence within the scope of its supported signals. | There is no basis here to treat a tool as a universal authority across generators and social-media transformations. An anomaly or automated result alone does not establish origin or truth. |
Content Credentials and C2PA
C2PA is a provenance standard for recording claims about an asset, including its origin and editing history. The Coalition for Content Provenance and Authenticity explains that Content Credentials do not make value judgments about whether provenance assertions are “true”; they provide a tamper-evident history mechanism that complements fact-checking and digital forensics. A missing credential does not prove that a file is camera-made: metadata may not have been preserved.
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Watermarks and supported verifiers
Watermarks are signals that compatible systems know how to check. OpenAI describes SynthID as a signal embedded in supported media and designed to withstand some transformations. Detection indicates likely origin from OpenAI tools, but not how the file was used or modified afterward. The OpenAI verifier checks supported OpenAI signals, not every company’s models. Google reported in 2025 that SynthID had watermarked more than 20 billion pieces of AI-generated content; that company-reported figure is not evidence that a general-purpose tool can recognize all of those images on social media.
Why a negative result is inconclusive
A checker can return no result because it does not support the generator or signal, because metadata was removed or changed, because a watermark was degraded, or because the content predates the signal. OpenAI says its verifier does not detect every other company’s model. “Not detected” therefore means no supported signal was found in the file submitted—not that a camera captured it.
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Verify the post, not just the pixels
Even a reliable indication of an image’s origin does not validate its caption. A genuine photograph can be old, taken somewhere else, or attached to a misleading account of events; an AI-generated illustration can accompany an accurate explanation. Check the account and publication date, compare the claim with independent reporting or primary sources, and look for earlier appearances or context. Provenance and image inspection complement fact-checking rather than replacing it.
For additional guidance, see the C2PA Explainer, OpenAI’s guidance on C2PA in ChatGPT images, OpenAI’s verification FAQ, OpenAI’s content provenance documentation, and Google’s announcement about SynthID verification and detection. Vendor features and supported signals can change; these descriptions should not be read as a feature-by-feature account of every social platform.
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