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Treat a no-result, “not detected,” or uncertain AI-image-detector response as inconclusive. It means that the particular service did not find a signal it supports or could not classify the file confidently; it does not prove that a person made the image, and it does not prove that an AI system made it.
Identify exactly what the tool checked, test the best original file you can obtain, inspect provenance separately, and gather contextual evidence. A probability score or missing watermark should never be the sole reason to accuse someone, remove content, deny a service, or make an employment, education, or disciplinary decision.
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Start with the exact wording of the result
Detector interfaces use different labels for different situations. Preserve the result as displayed instead of translating every message into “real” or “AI.” Record the service name, date, file used, and wording shown on screen.
- No signal or no result: the service did not find one of its supported provenance or watermark signals. The file may still be AI-generated, or the signal may have been removed, degraded, or never supported by that service.
- Not detected: absence of a detected signal is not a finding that the image was made by a human.
- Inconclusive or uncertain: the service could not reach a sufficiently confident classification. Treat it as a prompt for more evidence, not as a tie-breaker.
- Probability-like score: a statistical estimate from that model under its documented conditions. It can be wrong in both directions and needs corroboration.
OpenAI’s Verify documentation describes its own outcome precisely: “If no signal is detected, it means the tool did not find supported signals in the uploaded file.” That sentence is about the tool’s coverage, not about the image’s authorship.
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Why a detector can miss an AI-generated image
There is no single universal AI-image signal. Services inspect different evidence, and a result from one provider says little about signals that provider does not cover.
OpenAI Verify
Verify checks supported OpenAI-associated C2PA credentials and SynthID signals for supported images and audio. It is not designed to identify output from every image generator. OpenAI notes that a signal can be absent when a file is unsupported, predates the relevant signal, or has had metadata or a watermark removed or degraded. A missing Verify result therefore narrows what was found in that file; it does not establish human creation.
For the cleanest check, OpenAI recommends uploading one image at a time and avoiding cropping or format conversion. If you only have a social-media download, screenshot, thumbnail, or edited export, you may be testing a derivative that no longer contains information present in the original.
OpenAI Content Provenance API
The programmatic provenance service checks two different kinds of OpenAI-associated evidence. Its C2PA check looks for signed metadata in images; its SynthID check looks for an embedded watermark in images and audio. It is a provenance checker for supported signals, not a general-purpose detector that evaluates every model or every visual artifact.
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Google Cloud AI Content Detection API
Google Cloud documents a separate image service that uses machine-learning analysis of pixel-level artifacts, noise patterns, and spectral anomalies. The documentation says C2PA metadata detection is not included. Its listed image formats are JPEG, PNG, and WebP. The page labels the API Private Preview, so access and behavior can change.
Google describes the output as probabilistic. Its documentation states: “The API provides a probabilistic estimate and doesn’t guarantee definitive identification.” It also warns that false positives and false negatives occur and that the output should not be the sole basis for critical decisions.
Microsoft Content Provenance Detection API
Microsoft’s service checks supported Microsoft-created C2PA manifests and invisible watermark signals. Its documentation explicitly says that no detected provenance does not mean the media was not AI-generated. The page lists JPEG, PNG, GIF, and WebP images and a 100 MB limit. Those constraints apply to the documented service and should not be generalized to other detectors.
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C2PA Viewer
The U.S. Office of Research Integrity describes the C2PA Viewer as a free, open-source way to inspect Content Credentials. It can show a credential’s issuer and recorded history when a valid credential is present. It does not check SynthID or other watermark systems, so an empty viewer result is not a universal “human-made” finding.
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Use this six-step investigation sequence
- Capture the original result. Save the exact label or score, the service name, the date, and the file name. Do not paraphrase “no signal” as “authentic.”
- Read the service’s coverage statement. Determine whether it checks signed C2PA metadata, a provider-specific watermark, pixel-level artifacts, or a combination. Ask which generators, file types, and signal versions are supported.
- Obtain the best available original. Request the source file rather than a screenshot or platform download. Keep the file unchanged for the first check. Avoid cropping, resizing, recompressing, or converting formats because editing and sharing can remove metadata or weaken a watermark.
- Run the appropriate check again. With Verify, follow the one-image-at-a-time recommendation. With an API, confirm that the request uses a documented format and size. Record whether the original produces a different result from the derivative.
- Inspect provenance independently. Open any available C2PA Content Credential and examine the issuer, creation event, software, and edit history. Use a supported watermark check when the suspected provider offers one. Do not assume that a credential viewer checks every watermark technology.
- Gather context and escalate proportionately. Ask where the file came from, who supplied it, and whether earlier versions or publication records exist. Compare the image with corroborating documents, photographs, timestamps, or eyewitness information. If a decision could affect reputation, employment, education, benefits, or access to a service, obtain independent review rather than relying on one automated result.
How to interpret provenance evidence
A present credential or watermark
A valid C2PA credential can record origin and subsequent edits. The issuer and history may show which application signed the file and what transformations were recorded. A supported watermark can provide an additional provider-specific signal. These findings are useful for tracing a file’s technical history.
An absent credential or watermark
Absence has several possible explanations: the generator was outside the service’s coverage; the image was created before a signal was used; the file format was unsupported; metadata was stripped during editing or upload; or a watermark was degraded. Therefore, an empty result is evidence about the test, not a verdict about authorship.
What provenance cannot establish
Provenance answers questions such as “Where did this file come from?” and “Which edits were recorded?” It does not establish that the depicted event really happened, that the scene is presented in its original context, or that the accompanying claim is true. A genuine credential can travel with a misleading caption, and a file without credentials can still be an authentic photograph.
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A probability-like output is not a courtroom-style confidence level. It is a model estimate generated from the service’s training and operating assumptions. A high or low value can be wrong, especially after resizing, recompression, screenshots, stylistic editing, or unusual camera processing.
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Google Cloud’s documentation specifically describes both false positives and false negatives. No independently useful cross-tool accuracy percentage or universal detector ranking is established by the cited official documentation. Do not invent a threshold such as “above 80 percent means AI.” If a service publishes a threshold for a particular workflow, treat it as that service’s operational guidance, not a general law of image authenticity.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Common situations and the safest response
The image came from a social network or messaging app
Assume the copy may have been recompressed or stripped of metadata. Ask for the original export, camera file, or creator-held source. Preserve both versions, label them clearly, and test the original before drawing conclusions.
One service says no signal while another reports a signal
First compare what each service checks. One may inspect C2PA metadata, another a provider watermark, and another pixel-level artifacts. A disagreement can reflect different coverage rather than a contradiction about the image. Document the methods and seek provenance or contextual evidence that does not depend on either score.
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The detector reports “uncertain” on an edited image
Editing can change the very features a pixel-level model evaluates and can remove signed metadata. Return to the earliest available file, preserve the edited copy separately, and treat the uncertain result as expected evidence of limited observability rather than proof of manipulation.
The result could trigger a serious penalty
Pause the decision. Give the affected person an opportunity to provide the source file and context, obtain a second human review, and record the evidence considered. Automated output may be a lead for investigation, but it should not be the only basis for a sanction or public accusation.
What not to do
- Do not claim that “no result” means “real.”
- Do not claim that a detector score proves an image was generated by AI.
- Do not combine scores from unrelated services into a made-up average.
- Do not strip metadata, resize the image, or convert formats before the first provenance check.
- Do not treat a C2PA Viewer as a checker for SynthID or every other watermark.
- Do not publish a person’s name or impose consequences based only on an automated classification.
- Do not present a provider-specific signal as evidence that every AI system was ruled out.
A documentation checklist for defensible decisions
Keep a small evidence log so another reviewer can reproduce the assessment:
- Original file name, extension, byte size, and the person or system that supplied it.
- Every derivative tested, including screenshots or downloads, with the transformation noted.
- Service name, version or preview status when shown, date tested, and exact result wording.
- Signals covered by each service: C2PA, provider-specific watermark, pixel analysis, or another documented method.
- Credential issuer, manifest history, and any recorded edits.
- Independent context such as the publication source, earlier versions, timestamps, and corroborating records.
- The reviewer’s conclusion stated narrowly: what the evidence supports, what it does not establish, and what remains unresolved.
Current availability and changing documentation
Google Cloud’s documentation page was updated on 2026-10-09 UTC and labels its image API Private Preview. Preview access, supported formats, and behavior may change. Microsoft’s documentation page was updated on 2026-08-05. OpenAI’s Verify and Content Provenance documentation describes provider-specific signals rather than universal AI detection. Recheck the current documentation before building a policy or automated workflow around any one service.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchThe practical rule remains stable even as implementations change: a detector can report only what its supported signals and model can observe. Use the result to decide what to investigate next, not to skip the investigation.
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