An inconclusive AI image detector result means the available evidence does not support a reliable origin decision under that tool's rules. It does not mean “definitely real,” “definitely AI,” or an even chance of each. First check which analyses completed, then obtain the best file, inspect provenance, and investigate the source claim.
The goal is to reduce the uncertainty that matters. A second opaque score may add less than the original export, a recorded creation history, or an earlier publication showing that the caption is wrong.
Did the scan complete, or was a check unavailable?
Read the processing status and individual evidence sections before interpreting the verdict. An overall inconclusive result can coexist with useful completed checks. A failed upload or an unsupported analysis is a different problem and should be recorded separately.
| What the report says | What you know | What to do next |
|---|---|---|
| Completed, but Inconclusive | The supported analysis did not justify a stronger decision | Review evidence, limitations, and the original file |
| Verification unavailable | That evidence channel was not checked | Resolve availability or use a suitable verifier |
| Unsupported file or analysis | The input or requested check falls outside supported coverage | Obtain a supported version without discarding the original |
| Processing failed | The attempted scan did not finish successfully | Keep the error and retry after resolving its cause |
| No credential or watermark found | A completed supported check found no signal of that kind | Preserve the check's scope; continue with other evidence |
For AIFakeScan's current beta, passive detector fusion has not passed the calibration gate. Without a supported strong provenance or official verification signal, passive-only results remain Inconclusive. This is a product decision rule, not evidence that every submitted image is equally ambiguous.
Read the methodology and coverage limits alongside the AI image detector. Do not translate an internal model observation into a measured probability that the image is AI-generated.
Get the original file before changing the image
The next useful input is often a better documented file, not a more aggressive retry. Preserve the copy you received and ask for the original camera file or generation export. Keep both versions clearly labeled.
Research by Corvi and colleagues examines detection of diffusion-generated images, including social-network scenarios involving compression and resizing. It supports treating transformations as relevant evaluation conditions; it does not supply an accuracy guarantee for an unrelated online checker.
Ask how your copy was produced: downloaded from a source, saved through a messaging app, captured as a screenshot, cropped, or re-exported. Record what the source can confirm and what remains unknown.
Avoid sharpening, upscaling, or re-saving the only available copy before analysis. Those actions create another version to explain. If you compare versions, change one thing at a time and keep separate reports.
Our screenshot and compression study provides a limited worked example of file transformations. Use it as a case study rather than an accuracy benchmark for every screenshot or generator.
Inspect provenance and the declared action
Content Credentials can provide evidence about recorded creation or editing actions when available. Use the C2PA checker and read the validation status together with the signer and declaration. A missing record leaves an information gap.
The C2PA specification distinguishes actions and ingredients. An AI ingredient, an AI edit, and AI creation of the asset are different declarations. Record which one appears rather than describing all three as complete image generation.
Our local credential inspection does not retrieve remote manifests. If the source says provenance was attached but none is found, request the signed original and ask which inspection workflow they used.
Also check whether metadata supports a lead worth following, such as an application name or export history. Metadata should guide questions, not settle them: a software tag alone does not establish the complete creation process.
The practical question is specific: “What does the available record say about this file?” Keep the separate question of whether its caption or depicted event is accurate open until you investigate it.
Use a provider watermark check when the source fits
Choose a watermark workflow based on the suspected provider and supported media type. A verifier for one watermark system does not test every AI generator. A negative or unavailable result should keep that coverage boundary attached.
Google DeepMind's SynthID documentation describes watermarks applied in its generative AI products and a Gemini workflow for checking whether media was generated or altered by Google AI. Follow the current official instructions when that is the question you need to answer.
The site's SynthID checker page directs you to relevant workflows. It does not perform a direct SynthID scan of uploaded images. Use the linked official workflow and keep its supported media and provider scope attached to the result.
Save the provider's exact output. “A supported watermark was found” is a different statement from “this image's story is true.” “No supported watermark was found” is different from “no AI was used.”
Check the source claim independently
An image's origin and the claim attached to it are separate questions. Even when generation remains unresolved, you may be able to verify that an image predates the claimed event, was published elsewhere, or depicts a different place.
Use these checks as an investigation workflow:
- Write down the specific claim: who, where, when, and what supposedly happened.
- Locate the earliest publication you can substantiate, keeping its URL and date.
- Look for other views, nearby events, or independent reporting that supports the context.
- Ask the creator or publisher for the original and an explanation of the workflow.
- Separate confirmed facts from inferences and unresolved details.
NIST's digital-content transparency report discusses provenance tracking and synthetic-content detection as distinct approaches with context-dependent uses. It also notes that authentic content can still be misleading. A technical origin check therefore answers only part of a verification task.
If another detector disagrees, preserve the difference and investigate the tools' coverage. Do not average unrelated scores or turn a majority vote into proof. For the broader error problem, see why AI detector false positives happen.
Keep an evidence log and choose a useful stopping point
A short evidence log makes uncertainty actionable. Record the file version, completed checks, observations, and unresolved questions. Decide what further information could change your conclusion before spending time on repeated scans.
| Evidence log field | Example wording |
|---|---|
| File version | Screenshot from a repost; original not supplied |
| Processing | Scan completed; passive result Inconclusive |
| Provenance | No locally available credentials found |
| Watermark coverage | Official provider check not performed |
| Source context | Earliest confirmed publication recorded separately |
| Open question | Whether the source can supply the original export |
| Next action | Request the original and review the declared workflow |
These entries illustrate a template, not a report from a tested file. Replace them with your actual observations.
A useful stopping point is a bounded conclusion: “Origin remains unresolved for this copy; the caption has been independently checked,” or “The source supplied a validated record declaring AI editing.” If no available check addresses the unresolved question, more retries may simply repeat the same limitation.
For a consequential decision, preserve the evidence and obtain qualified review appropriate to the case. An inconclusive result should remain inconclusive until additional evidence supports a narrower, defensible statement.
Frequently asked questions
Does Inconclusive mean the image is probably real?
No. The result does not establish a human origin. Read the decision reason and coverage: uncertainty can reflect insufficient evidence, unvalidated model interpretation, or limitations of the file and supported checks.
Is an inconclusive scan a technical failure?
Not necessarily. A scan can complete successfully and still lack evidence for an origin decision. Check the processing status and each evidence channel to distinguish uncertainty from failed or unavailable analysis.
Should I keep uploading until I get a confident result?
Repeat only when something meaningful changes, such as obtaining the original or resolving an unavailable check. Keep both reports. Repeating the same supported analysis does not by itself add independent evidence.
What should I share with someone reviewing the image?
Share the exact file version, source, scan date, result wording, completed checks, and limitations. Include any independently verified context. Keep observations separate from claims that still need investigation.