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You cannot reliably identify every AI-generated image, audio clip, or video by sight or sound alone. Start with any platform disclosure or provenance record, use a verifier only for the specific signals it supports, and then check the original source and claim independently. Treat visual or audio oddities and detector results as clues—not proof that a file is synthetic or that its contents are false.
Check an image, audio clip, or video in this order
1. Look for a disclosure from the platform or publisher
Check the post, its caption, and the platform’s surrounding context for an AI-generated or AI-edited label. Labels are useful leads, but platforms do not all display them the same way. Ofcom’s attribution toolkit describes approaches including persistent labels, labels revealed after viewing, and notices shown during a clip. A label may depend on a creator’s disclosure or a platform’s detection practices; no visible label is not proof that media was made without AI.
2. Inspect provenance credentials if the file has them
Content Credentials can record declared information about an asset’s origin and edit history: for example, whether it was generated, AI-enhanced, captured by a device, or edited by a person. They may also describe actions or regions affected. The C2PA implementation guide explains this provenance approach. A credential is a record of provenance, not a certification that the scene, speaker, caption, or accompanying claim is truthful. Sharing, editing, or converting a file can also remove its metadata.
3. Check for a supported watermark or provenance signal
If a compatible verifier is available, use it on the original file and read what signal it actually checked. A positive result can support a narrow conclusion about that signal; it does not establish whether the depicted event happened as described. A negative result is inconclusive: the file may have been transformed, the watermark may be degraded, the media may predate a signal, or the tool may not support the generator involved.
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4. Check the source and claim independently
Look for the earliest available copy, the account or outlet that first published it, and independent reporting or documentation. Compare the date and location with reliable records, and look for original footage or a recording where possible. A provenance record can describe an asset’s recorded origin and edits, but cannot establish the accuracy of its caption or the intent of the person sharing it. For consequential claims, ask a qualified newsroom or forensic specialist rather than making a serious accusation from a visual artifact or automated score alone.
What to examine in the media itself
Look for whether details remain coherent across the file, but do not treat any single mismatch as a reliable AI tell. Compression, editing, low-quality recording, and ordinary camera or microphone effects can also produce odd-looking or odd-sounding details.
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Images
Inspect the image at its original resolution if you can. Consider whether objects, text, shadows, and scene details fit together, then compare the image with earlier or independently documented versions. An apparent inconsistency is a reason to investigate the source—not a verdict about how the image was made.
Audio
Listen for continuity in the voice, background sound, and transitions, and compare the clip with a reliable recording or transcript if one exists. A short, edited, or noisy clip gives you less context to assess. A convincing voice alone does not establish who recorded it or whether the words are authentic.
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Video
Check whether motion, speech, and sound remain consistent from one moment to the next. Compare different points in the clip rather than judging a single paused frame, and seek the original or a longer version when available. A visual or lip-sync mismatch can be a clue, but edits, dubbing, and recording conditions can also create discontinuities.
What different detection methods can establish
| Method | What it can indicate | What its result cannot establish on its own |
|---|---|---|
| Content provenance / Content Credentials | A signed, machine-readable record of declared origin and edit history, when present and valid. See the C2PA implementation guide. | That the scene or caption is true; transformations can strip metadata. |
| Embedded watermark, such as SynthID | Whether a compatible tool finds a supported watermark signal in media covered by that watermark system. | Whether all AI systems or all synthetic media are covered. A negative result does not rule out AI generation. |
| Fingerprinting | Whether a file matches known or related media, which can help identify reused material. | Whether the original matched media was AI-generated. |
| AI-content detector | A classifier’s estimate that an input resembles media covered by its design or training. | A universal judgment. Before relying on a result, check which generators and media types the tool supports, how it reports uncertainty, and which transformations it has been evaluated against. |
These approaches answer different questions, so there is no single accuracy ranking that applies to every tool and file. The European Commission’s May 8, 2026 study summary covers marking and detection across modalities and considers effectiveness, limitations, and practical applicability. Microsoft Research’s overview likewise distinguishes secure provenance, imperceptible watermarking, and soft-hash fingerprinting by their purposes and protections.
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What named verification tools support
OpenAI Verify
OpenAI Verify checks uploaded images and audio for supported OpenAI-associated C2PA and SynthID signals; it is not a general-purpose detector for every provider. OpenAI recommends checking one file at a time, avoiding image crops or conversion, and using audio clips that are 10–60 seconds long. A result with no supported signal does not rule out OpenAI generation or generation by another provider.
OpenAI Content Provenance API
The OpenAI API guide says the API checks supported OpenAI signals: C2PA for images and SynthID for images and audio. The documentation explicitly describes it as not a general-purpose detector.
Google verification
On May 19, 2026, Google announced SynthID verification for image, video, and audio through Gemini, with expansion to Search and Chrome, and described C2PA checks rolling out first in Gemini and then in Search and Chrome. These were rollout statements, not a guarantee that every feature is available in every location or account today; check the current product interface. Google also announced an enterprise AI Content Detection API for organizations, which is distinct from a consumer verification tool. In the same announcement, Google reported that SynthID had watermarked more than 100 billion images and videos and 60,000 years of audio, and that Gemini verification had been used 50 million times globally. Those are reported deployment and usage figures, not measures of detection accuracy. See Google’s announcement.
Quick Recap
How to weigh the evidence
- A label or valid credential: evidence about a disclosure or recorded provenance, not proof that the content is accurate.
- A supported verifier match: evidence about the signal that particular tool checks, not a universal judgment about all AI media.
- No label, credential, or match: not proof that the media is human-made; the relevant evidence may be missing, stripped, degraded, or unsupported.
- A visual or audio inconsistency: a reason to check the file’s history and context, not enough on its own to accuse someone of fabrication.
- Independent corroboration: the strongest way to assess what the media actually shows or claims, regardless of how it was produced.
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