Evidence-led image review guide

How an AI Image Scanner Evaluates Evidence

Understand how image scanners evaluate provenance, metadata, and model signals associated with AI generation or editing. The live scanner is not currently available.

Educational content · No image upload · No live classifier

Evidence ledger

What the image scanner checks

An image can lose metadata when it is screenshotted, compressed, cropped, or reposted. A visual classifier can also be wrong. These guides keep different kinds of evidence separate.

01

Provenance

Check for Content Credentials and other signed origin information when present.

02

File Metadata

Review software, encoding, dimensions, and modification clues without treating missing metadata as proof.

03

Model Inference

Use a visual classifier as one signal, with model information and limitations shown beside the result.

Process

How the AI image scan works

  1. 1

    Upload or paste an image

    Choose a supported image from your device or clipboard.

  2. 2

    Inspect multiple evidence layers

    Check available provenance, file metadata, and model-based visual signals.

  3. 3

    Review evidence and uncertainty

    Understand why a conclusion was reached and what cannot be determined.

Illustrative result anatomy

What a careful result should explain

This is an educational example, not an analysis of an uploaded image and not output from a live provider.

01

Observation

State what was actually found, including when evidence is missing or unavailable.

02

Interpretation

Explain what the observation may support without turning uncertainty into proof.

03

Limitation

Identify transformations, missing context, and other reasons the signal may be wrong.

Seven-state contract

Results designed for careful decisions

None of these states should be treated as proof of authorship, intent, fraud, or misconduct.

01

Direct AI provenance found

Recognized signed provenance or a generator declaration is present.

02

Likely AI-generated or AI-edited

Multiple available signals are consistent with AI generation or editing.

03

Mixed or uncertain

Available signals disagree or are too weak for a clear conclusion.

04

Likely camera or human origin

Signals lean toward camera or human origin, but AI involvement cannot be ruled out.

05

Insufficient evidence

The file does not contain enough reliable evidence for a useful conclusion.

06

Unsupported or unreadable

The file could not be safely parsed or is not supported.

07

Scan not completed

The scan ended before a result could be produced.

Interpret carefully

Why screenshots and edits are harder to assess

Screenshots replace the original file container. Cropping, recompression, and social platforms may remove signed provenance and camera metadata. A missing signal therefore cannot establish human origin, while model inference may be less reliable after transformation.

High-stakes boundary

Do not use a scan result as the sole basis for discipline, grading, employment, credit, housing, legal, or other high-stakes decisions.

Current availability

Learn now; upload later

The public site is intentionally content-only. No image upload, classifier request, account, payment, or advertising is active. Privacy-friendly aggregate analytics measures site usage without analytics cookies.

  • Read the evidence methodology
  • Understand common failure modes
  • Review all seven result states
  • Check the current privacy boundary
Read the Methodology

Questions

AI scanner questions

Can an AI scanner prove that an image was generated by AI?

No. A scanner can surface provenance, metadata, and model-based signals, but those signals may be incomplete or wrong. Treat the result as one part of a broader verification process.

Can I upload an image now?

No. The public site currently has no image upload or live classifier. The feature remains disabled while provider and data-handling terms are reviewed.

Does FreeAIScanner require an account?

No account is required to read the current educational site, and no account feature is active.

What does it mean if an image has no metadata?

Missing metadata does not mean an image is human-made. Social platforms, screenshots, editing tools, and compression can remove or change metadata.

Can the scanner detect AI-edited real photos?

It may surface signals associated with AI editing, but coverage varies by tool, edit, and file history. Review the evidence and limitations shown with each result.

Can I use a result to accuse someone of cheating or fraud?

No. The result is not proof of authorship, intent, cheating, or fraud. Use source verification and qualified human review, especially for consequential decisions.

Verify before you decide

Understand the evidence before trusting a result

Learn what provenance, metadata, and model inference can—and cannot—tell you.

Read the MethodologyReview Privacy →