Visual oddities, unusual voices, polished prose, and detector scores can all be reasons to investigate—but none proves by itself that content was generated by AI. When possible, preserve the original file and check its provenance, publisher, and independent context. Keep three questions separate: Was a generative system involved? Is the content accurate? Who made it or owns it? Evidence that helps answer one does not necessarily answer the others.
What clues can—and cannot—show
There is no validated human checklist that reliably identifies AI-generated images, audio, and text in every case. A mistake in an image, an unnatural-sounding phrase, or repetitive writing may prompt a closer look; it is not proof. Real media can look unusual because of editing, compression, recording conditions, or the subject itself, while generated media can be polished or altered to remove obvious clues.
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Automated classifiers have the same basic limitation: their results depend on the material and systems they were tested on. A score is a signal to interpret, not a finding about a particular creator. Provenance records and watermarks answer a different question by indicating whether supported origin information or a supported signal is present.
How to check a suspicious image
- Look at the whole image, then inspect details. Check text, reflections, lighting, hands and other small features, and whether objects fit the scene’s geometry. Treat inconsistencies as clues, not a verdict; camera artifacts, editing, compression, and unusual scenes can produce apparent inconsistencies too.
- Look for the earliest available source. Search for the image in its original publication or a traceable context, and compare captions, dates, and surrounding reporting. A repost without context is weaker evidence than a source that can explain where and when the image was made.
- Preserve the original file and check its provenance if available. A compatible verifier may show whether the file carries a supported provenance record or watermark. An image that has been resized, edited, or shared through a service may have lost metadata; a negative check does not establish that a person made it.
OpenAI says supported images generated with ChatGPT, Codex, or its API include C2PA and SynthID signals. That is provider-specific coverage, not a universal image detector. In remote identity-proofing guidance, NIST says image algorithms should be tested on both forged and genuine media, with false-positive and false-negative rates documented and manual review added. That guidance concerns identity proofing, not an endorsement of consumer image-checking tools.
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How to check suspicious audio
- Listen for mismatches, not a single tell. Timing, pronunciation, breathing, background sound, or emotional continuity may warrant scrutiny, but none alone proves a voice was synthesized.
- Compare with independently sourced material. Check whether the statement appears in a reliable transcript or recording, and whether the clip’s context supports the claim. A familiar voice impression is not enough to establish who spoke.
- Verify consequential claims through another channel. For a suspected impersonation or fraud attempt, contact the person or organization using contact details you already trust—not a number or link supplied with the suspicious message.
OpenAI describes SynthID watermarks in supported audio generated by ChatGPT or its API. Its checker looks for supported OpenAI signals; it does not claim to identify every vendor’s synthetic audio. Check results therefore depend on the file format and signal the verifier supports.
How to assess suspicious text
- Check the substance. Verify claims against source documents, follow links, and confirm quotations in their original context. Whether a passage is accurate is separate from whether AI helped write it.
- Do not treat style as authorship evidence. Generic structure, repeated transitions, or smooth but thin explanations can appear in human writing as well as generated prose; generated writing can also be edited.
- Be cautious with classifier results. OpenAI says access to its text detector is limited to approved research organizations. It also notes that short text, constrained factual wording, language, and transformations affect watermark detection. A classifier result should not be the sole basis for an accusation or disciplinary decision.
What detector research establishes—and what it does not
NIST’s report on its 2024 GenAI text-to-text pilot, published June 25, 2025, found substantial variation across the systems tested: some generators could deceive most tested discriminators, while some discriminators could detect outputs from almost all generators. NIST’s GenAI program page also reports that three generators in the initial text summarization pilot fooled every detector. Those findings concern particular participants and tasks; they are not a universal success rate for current tools. Results from different rounds or systems can differ without contradicting one another.
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NIST frames evaluation in terms of error trade-offs, including true-positive rate at a chosen false-positive rate. When reviewing a detector’s claims, ask what generators and content it was tested on, what human-written comparison material it used, what false-positive rate applies, and whether the tested material resembles the file in question. A confident-looking score without those details cannot identify the author of a particular item.
OpenAI reports language-level results for its text watermark detector across official EU languages: at a stated 1% false-positive rate, detection was 69.0% for Spanish and 42.2% for Romanian. These are OpenAI-reported results for that evaluation—not figures for all text detectors, languages, or writing tasks.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How provenance, watermarks, and classifiers differ
C2PA is an open standard for certifying source and history information associated with media. A Content Credential can provide a signed provenance record and show what that record says. A classifier instead analyzes content for patterns associated with its training or test conditions, typically returning a score. A watermark verifier checks for a supported embedded signal. None of these methods, on its own, decides whether a claim is true, whether a presentation is fair, or who legally owns the work.
| Method | What a result can indicate | Important limit |
|---|---|---|
| Forensic classifier | A score or classification based on patterns in the supported media it analyzes. | Performance and error rates depend on tested content and systems; a score does not establish authorship. |
| C2PA Content Credentials | Whether a compatible file carries a signed provenance manifest and what information it records. | It depends on a record being created and retained. Missing credentials do not prove human origin, and the record does not establish truth or ownership. |
| Watermark verifier | Whether the verifier detects a signal it supports in a supported file. | It is not a universal check for every provider. Editing, sharing, or other transformations may affect detection. |
OpenAI cautions that its supported signals may be absent if a file is unsupported, metadata was removed, or a watermark was degraded. A positive signal is also limited: it does not establish accuracy, legal ownership, the identity of the creator, context, or how much a person contributed. As OpenAI puts it, “Provenance signals can provide useful information about origin, but they do not guarantee that content is accurate, unedited, legally owned, or presented in the correct context.”
Quick Recap
A proportionate way to reach a decision
- Preserve the file you received. Keep the original when possible rather than relying only on a screenshot, converted copy, or repost.
- Check source and context. Find the publisher or earliest traceable version and look for independent evidence that supports or contradicts the claim.
- Use compatible provenance checks where available. Record what the verifier supports and what it found; do not treat a missing signal as proof of human creation.
- Use detector outputs as one input. Consider the tool’s stated test conditions and error trade-offs, and avoid applying a result beyond the media, systems, or language it covers.
- Escalate consequential cases to human review. If the outcome could affect someone’s identity, reputation, safety, or livelihood, combine technical checks with independent evidence and explain what remains uncertain.
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