The Tool Desk
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Start with clues, not a verdict
Generated images may contain details that do not quite fit together. A research guide groups common visual artifacts into five categories: anatomical, stylistic, functional, physical, and sociocultural. Those categories are useful prompts for a closer look, not a reliable checklist that can prove an image’s origin.
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- Anatomical: Look for inconsistent body parts, faces, or object boundaries.
- Stylistic: Notice details that seem mismatched or inconsistent within the image.
- Functional: Ask whether objects appear to interact with each other and the scene in a plausible way.
- Physical: Check whether perspective, lighting, shadows, highlights, and reflections agree.
- Sociocultural: Consider whether clothing, signs, objects, or other contextual details fit the claimed place and time.
Zooming in can reveal a detail worth checking, but an unusual feature is not proof of AI generation. Real photographs can show strange scenes, while convincing synthetic images may have no obvious artifacts. In a 2024 guide, the authors examined 138 AI-generated images, nine images curated from social media, and 42 real photographs; those counts describe the guide’s image set, not a universal detector-accuracy result. The guide also notes that artifacts are not always visible.
Use this workflow to check a social post
- Pause and capture the claim. Before sharing a shocking or emotionally charged image, note exactly what the caption says happened. Check whether the post presents the image as a photograph, illustration, or AI creation.
- Inspect details that should agree. Zoom in on anatomy, object edges, repeated patterns or text, and how objects meet or overlap. Check perspective, shadows, highlights, and reflections. Treat anything odd as a question to investigate, not a detector score.
- Check who posted it and when. Look at the original account or source, publication date, location, and any changes to the caption. See whether trustworthy sources independently report the same event.
- Look for provenance information. Check for a platform disclosure or Content Credentials. If you can obtain the original file, use a verifier that explains which signals, formats, or generators it supports. Keep the original when possible: editing, converting, or sharing may remove metadata.
- Interpret the result narrowly. “Detected” means the tool found a signal it supports; it does not prove the caption is accurate or reveal every edit made afterward. “Not detected” means no supported signal was found, not that a camera captured the image.
- Raise the bar for high-stakes claims. Do not accuse someone or publish a consequential claim based only on appearance or an automated score. Seek independent corroboration or a qualified media-forensics review.
What visual inspection, credentials, and watermarks can tell you
These approaches look for different kinds of evidence. Visual inspection considers what is visible; provenance records and watermarks can provide signals about a file’s association with a source or system. None alone answers whether the post’s claim is true.
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| Approach | What it can indicate | Coverage and limits | What you can conclude |
|---|---|---|---|
| Visual inspection | Visible anomalies or implausible details | Clues may be absent from synthetic images and may appear in genuine photographs. | A detail may warrant investigation; it does not establish the image’s origin. |
| Content Credentials (C2PA) | Signed provenance assertions about origin, edits, or AI use | Depends on credentials being present and preserved. Metadata can be lost during editing or sharing. | Can provide a tamper-evident provenance record; it is not a judgment that the assertions or the post’s claim are true. |
| Model-specific watermark | An embedded signal checked by a system that supports it | Coverage depends on the vendor, supported models, and signal. Transformations may affect detection. | A detected signal can associate a file with a supported system; it does not establish how the image was used or modified afterward. |
| Automated visual detector | A system’s assessment of an image | Do not assume a tool covers all generators or social-media transformations. OpenAI’s verifier checks supported provenance signals, not every company’s model. | Read the tool’s stated scope; a score or unsupported result is not a universal authenticity verdict. |
Content Credentials record provenance, not truth
C2PA Content Credentials are cryptographically bound provenance records that can carry assertions about an asset’s origin, modifications, and AI use. The standard checks whether information is well-formed, valid, associated with the asset, and signed by an issuer that may be checked against a trust list. C2PA explicitly does not make a value judgment about whether the provenance assertions are true; it provides a tamper-evident history mechanism that complements fact-checking and digital forensics. Read the C2PA Explainer.
Watermarks cover supported systems
OpenAI describes SynthID as a signal embedded in supported media and says it is designed to withstand some transformations. A detected signal indicates likely origin from OpenAI tools, but does not say how the file was used or whether it was modified afterward. The public verifier checks supported OpenAI signals; it is not a general-purpose detector for every AI system. OpenAI says a result with no detected signal does not rule out OpenAI generation or generation by another provider. OpenAI’s image-provenance information and API documentation describe these limits.
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Google announced in 2025 that more than 20 billion pieces of AI-generated content had been watermarked with SynthID. That is a company-reported adoption figure, not evidence that a general-purpose tool can recognize 20 billion images circulating on social media. Google’s announcement describes its own systems and should be read within that scope.
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A negative result is inconclusive. The checker may not support the image’s generator or format; metadata may have been removed or changed; a watermark may no longer be detectable; or the image may predate the signal. OpenAI specifically says its verifier does not detect every other company’s model. Its FAQ explains that no signal means only that the tool did not find a supported signal in the uploaded file.
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Social-media copies can also differ from the original file. If you can, check provenance on the original rather than a screenshot or re-saved copy, and keep in mind that not every platform preserves metadata. A missing credential or watermark should not be treated as evidence that an image is camera-made.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Verify the post’s claim separately
An image can be AI-generated and accurately captioned, or a real photograph can be old, mislabeled, or presented out of context. Establishing an image’s origin is a different task from verifying what it depicts or whether the accompanying claim is correct.
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- Find the earliest available post or source and check its date and account.
- Compare the caption’s specific claims—such as location, event, and timing—with independent reporting or primary sources.
- Look for earlier appearances or other context when available.
- For consequential decisions, seek corroboration beyond the image and its provenance signals.
Provenance tools, visual inspection, and fact-checking complement one another; none replaces verification of the claim itself. The First Draft visual verification guide offers further context on checking images.
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