The Tool Desk
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A positive watermark or provenance signal can show that a covered AI system created or edited some media. A missing signal does not prove camera capture, and neither signal type establishes that the depicted event really happened.
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Start by separating the questions
People often ask whether a file is “real” as though authenticity were one property. In practice, you may be asking three different questions:
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- What is the file’s history? Was it captured by a camera, generated from a prompt, or edited after capture?
- Can a system be attributed? Does the file contain a Content Credential or watermark associated with a particular AI tool?
- Is the claim true? Did the event, person, place, date, or quotation shown in the media actually occur as described?
These answers can diverge. A genuine photograph can be reposted with a false location. A real video can receive an AI cleanup or face replacement. A synthetic image can accurately illustrate a real event without being evidence of that event. Treat “AI involved” as a description of production, not as a synonym for “false.”
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Use the strongest available file and record what each check establishes. The goal is a documented confidence level, not an artificial yes-or-no verdict.
The six-step verification workflow
1. Preserve the best available copy
Start with the original image or video file whenever possible. A screenshot, social-media preview, screen recording, or re-export may have lost metadata, altered pixels, removed a provenance manifest, or changed the compression. Ask the sender for the file they downloaded or exported directly from the camera or editing application.
Keep that file untouched and make a separate working copy for inspection. Save the post or message that accompanied it, including its caption, account name, and publication time. If you only have a compressed copy, note that limitation before interpreting any negative result.
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Content Credentials based on C2PA can record a signer, the stated origin, and subsequent edits. Open a compatible viewer or the service that issued the credential and check whether the manifest is valid, which organization signed it, and what operations it lists.
A valid credential is evidence about the file’s recorded history, not a declaration that the scene is true. It may document ordinary camera capture, non-AI editing, or AI-assisted work. An invalid, unsupported, or missing manifest is not equivalent to a verified camera original: credentials can be stripped or altered when media is copied, converted, or uploaded.
Google’s documentation also notes compatibility requirements, including Content Credentials version 2.2 and later from products on its conforming list. A file that uses another version or an unsupported format may not display a history even when one existed.
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3. Use a watermark checker that covers the suspected system
Watermarks are designed signals embedded in pixels, frames, or audio. They are not universal. Use the checker associated with the model family you are investigating rather than treating one service as an all-purpose detector.
Google’s SynthID tools look for supported Google or partner watermarks. OpenAI’s public checker looks for supported OpenAI-associated provenance signals, including compatible C2PA metadata and SynthID signals. A positive result supports a narrow attribution: some or all of the checked media was created or edited with a covered system. It does not prove that the entire file was generated from scratch, that every frame contains the mark, or that the caption is accurate.
For a video, read the reported time range carefully. Gemini may identify portions in which a watermark was detected; Google cautions that this does not mean every frame in that portion contains the signal.
4. Trace the source and surrounding context
Run a reverse image search on the highest-quality still you have, and use several distinctive frames from a video. The purpose is to find earlier appearances, a higher-resolution original, or a source account—not to obtain an automatic authenticity certificate.
Compare the earliest publication date with the date claimed in the caption. Examine the account’s posting history, stated location, cropping, translations, and edits. Look for independent reporting, official records, weather or geographic details, and unrelated footage from the same event. An image that first appeared years earlier, or in another country, may be authentic media used in a false context.
5. Treat visual anomalies as prompts, not proof
Zoom in on small text, signs, logos, hands, teeth, jewelry, reflections, and background objects. Google lists garbled lettering, inconsistent anatomy, repeated patterns, mismatched shadows or lighting, and errors in fine details as common warning signs.
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These clues are useful for deciding what to investigate next. They are not a dependable checklist. Compression, motion blur, night scenes, unusual lenses, and aggressive sharpening can produce similar artifacts in genuine media, while newer generators may avoid older telltale errors.
6. Corroborate consequential claims independently
If the media could affect safety, elections, financial decisions, a person’s reputation, or an emergency response, verify the event separately. Seek independent video, contemporaneous reporting, public records, eyewitness material with a traceable origin, or statements from organizations that were present.
Check whether the claimed time, place, weather, shadows, language, and sequence of events agree. A provenance signal can tell you about production; only independent evidence can establish what happened in the world.
How to interpret the evidence
| Result | What it supports | What it does not establish |
|---|---|---|
| Supported positive watermark | Some or all of the checked media was created or edited by the covered AI system. | That the story is false, that the whole file is synthetic, or that the depicted event did not happen. |
| No watermark detected | The service found no signal it supports in the submitted media. | Camera capture, human authorship, or authenticity. Another model may use a different watermark, or the signal may have been removed or degraded. |
| Valid Content Credential | A signer attests to an origin and an edit history recorded in the manifest. | That the signer is truthful about the scene, or that the file’s caption is correct. |
| Missing, invalid, or unsupported credential | Only that no usable provenance record was verified. | That the file is synthetic or that it came directly from a camera. |
| Reverse-search match | A lead about where and when a similar file appeared, and possibly its original source. | That the earliest result is genuine or that the current caption is accurate. |
| Visual artifacts | Reasons to inspect the source, metadata, and corroborating evidence more closely. | A reliable diagnosis of AI generation on their own. |
| Conflicting or inconclusive checks | That the available evidence has coverage, quality, or interpretation limits. | A forced binary verdict. Preserve the file and report the uncertainty. |
Google’s Gemini help states: “If a SynthID watermark is detected, it means all or part of the image or video was created or edited by Google’s AI models.” Read “all or part” literally. The statement attributes production; it does not verify truth or context.
What current checking services can and cannot do
Google Gemini and SynthID
Google’s Gemini help allows users to upload supported media and ask whether an image, video, or audio file was created or edited by Google AI. Gemini currently recognizes content created by Google AI tools and can also surface compatible Content Credentials that describe origin and history.
The help page describes a rolling allowance of approximately 10 image checks, 10 video checks totaling up to five minutes, and 10 audio checks totaling up to three hours per 24 hours. These limits and supported formats can change, so check the live service before relying on it for a time-sensitive investigation.
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On October 7, 2026, Google DeepMind said its public SynthID Detector was expanding globally in English and could check images, video, and audio made with Google or partner AI. The announcement named OpenAI, NVIDIA, and Kakao as partners and said Apple support was planned. It did not claim coverage of every AI model. Google reported that its systems had watermarked more than 180 billion images and videos and 240,000 years of audio, and that verification features in Search, Gemini, and Chrome handled more than one million requests daily. Those are company-reported scale figures, not independent measurements of detection accuracy.
OpenAI Verify
OpenAI documents a public checker for images and audio. It looks for supported C2PA metadata and SynthID signals associated with OpenAI tools, so it is not a general detector for media made by every provider.
OpenAI says submitted files are processed for signal checking and are not stored unless legally required or used to train its models. Review the live service’s accepted formats and current functions before uploading sensitive material. A result applies only to the signals and media portions the checker supports.
OpenAI Content Provenance API
The Content Provenance API guide describes image and audio checks for supported OpenAI provenance signals. Its not_detected result means that no supported signal was found; it does not rule out OpenAI generation, another AI system, metadata removal, or a real-world capture.
The guide explicitly describes the API as not being a general-purpose AI detector. Developers should present the response as one input to an investigation, not as an authenticity label.
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Ofcom’s discussion paper Deepfake Defences 2: The Attribution Toolkit distinguishes four related approaches: invisible watermarks, provenance metadata, visible AI labels, and contextual annotations. It is a discussion paper rather than formal regulatory guidance.
Ofcom describes provenance metadata as a way to record a creator, tools, and editing history, while a watermark may provide a narrower identifier. It also discusses interoperability, access restrictions, removal or degradation after editing, and the risk that people overinterpret either a label or its absence. A responsible assessment therefore combines attribution signals with source investigation and event-level corroboration.
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When more than one service is available, compare the following properties instead of asking which one is “most accurate” in the abstract:
| Comparison point | Questions to ask |
|---|---|
| Coverage | Which model, watermark, or signer families does it recognize? Does it cover the system suspected in this case? |
| Media support | Does it accept the file type, codec, resolution, duration, and version you have? Can it inspect the relevant video segment? |
| Signal type | Does it inspect embedded pixels, signed metadata, or both? Are those signals preserved in your copy? |
| Explanation | Does the result show validation state, signer, edit history, or the time range where a signal appeared? |
| Handling of uploads | How are files processed, retained, or used? Does the service’s policy fit the sensitivity of the material? |
| Limits | Are there daily quotas, language restrictions, partner-only coverage, or unsupported transformations? |
Two negative results from services that cover the same narrow signal still do not create universal proof. They only reduce the chance that those particular supported signals are present in the submitted copy.
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Practical examples of careful wording
A positive result on a breaking-news image
Report that the checker detected a watermark associated with the covered AI system and identify whether the result applies to the whole image or a portion. Then investigate where the image first appeared and whether independent sources confirm the event. Do not write that the watermark proves the event is fabricated.
No result on a widely shared video
State that the tested service found no supported watermark or credential. Note the file’s quality and whether it was downloaded from a platform that commonly re-encodes video. Continue with source tracing and independent footage; do not call it camera-original solely because the check was negative.
A credential showing AI editing of a real photograph
Describe the recorded operation precisely, such as an AI edit listed in the history. The credential may support the conclusion that editing occurred while leaving the underlying scene partly or wholly photographic. Compare the edited file with an earlier version if one is available.
Common mistakes that produce false confidence
- Calling a detector universal. Every service has a supported signal set, file limits, and coverage gaps.
- Treating absence as evidence of capture. Metadata can be stripped, watermarks can degrade, and older or unsupported generators may never have added a signal.
- Equating provenance with truth. A valid manifest records what a signer reports about file history; it does not independently verify the scene or caption.
- Using visual “tells” as a verdict. Blur, resizing, lighting, and ordinary editing can mimic artifacts, while synthetic media can look clean.
- Ignoring mixtures. A real recording can contain an AI replacement, enhancement, dubbed audio, or inserted object. Classify the affected part rather than forcing the whole file into one category.
- Checking only the repost. Platform previews and screenshots may have removed the evidence that the original contained.
- Uploading sensitive material casually. Read the current handling terms of the chosen service, especially for private people, unpublished footage, or legally protected information.
- Confusing popularity with corroboration. Thousands of identical reposts can trace back to one unverified account.
A decision framework for reporting your conclusion
After completing the checks, write a conclusion that names the evidence and its limits. Useful categories include:
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- Provenance verified: a valid credential documents a specific origin and edit history, with no unresolved validation problem.
- AI attribution supported: a covered watermark or manifest indicates that a named system created or edited some of the media.
- Source corroborated, provenance unknown: independent evidence supports the event, but no supported provenance signal survives in the file.
- Miscontextualized: the file is genuine or previously published, but the current date, place, person, or event claim does not match the source history.
- Inconclusive: the file is too compressed, unsupported, altered, or poorly sourced to justify a stronger statement.
Keep the wording proportional: “A supported SynthID signal indicates Google AI editing” is precise; “This proves the video is fake” is not. Likewise, “No supported watermark was detected in this copy” is accurate, while “The camera captured it” goes beyond the evidence.
Checklist before you share a verdict
- Did you obtain the original file rather than a screenshot or preview?
- Did you preserve an untouched copy and the original caption and account context?
- Did you check the credential’s signer, validity, compatibility, and edit history?
- Did you use a service that actually covers the suspected model or watermark family?
- Did you record which image, audio segment, or video portion was examined?
- Did you search for earlier appearances and compare dates and locations?
- Did you treat visual anomalies as clues rather than proof?
- For a consequential claim, did you find independent evidence of the event?
- Does your final wording distinguish production history from factual truth?
The most defensible outcome is sometimes uncertainty. Preserving the file, documenting the checks, and explaining what remains unknown is more reliable than assigning a confident label that the available evidence cannot support.
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