Provenance verification
Asks whether a signed claim exists, whether it still matches the asset and how the signer is evaluated.
- Cryptographic validation
- Declared actions and ingredients
- Certificate-chain context
- Clear “No credentials” outcome
Provenance vs classification
C2PA vs AI Detection: two different questions
C2PA validates available signed provenance evidence. AI detection estimates whether content may have been generated or modified by a model. Neither approach replaces the other.
For consequential decisions, report the underlying evidence or detector confidence—not a simplified “real” or “fake” label.
The core distinction
Evidence of origin is not an estimate of generation
Choose the technology according to the question, then communicate its limitations.
Synthetic-media detection
Asks whether statistical patterns resemble content produced or modified by machine learning.
- Probabilistic classifier score
- Vendor- and model-dependent behavior
- Possible false positives and negatives
- Sensitivity to processing and new generators
| Question | C2PA verification | AI detection |
|---|---|---|
| Primary input | Embedded or remotely referenced signed claims | Patterns in pixels, audio, video or text |
| Type of result | Validation and trust status | Probability or classifier label |
| Can identify declared signer? | Sometimes, through certificate and assertion context | No |
| Works when credentials are absent? | Returns No credentials | Can still produce an estimate |
| Proves factual truth? | No | No |
| Main failure mode | Missing, stripped, invalid or unrecognized evidence | False positives, false negatives and model drift |
Combined workflow
Use both signals without collapsing them
A review system can combine provenance, detection and source verification while preserving the meaning of each result.
Verify credentials and record the exact C2PA status.
Inspect signer, actions, ingredients and warnings.
Record model, version, score and known error rates.
Escalate uncertain or consequential cases to human review.
Interpretation examples
What common combinations mean
A result becomes useful only when it is described precisely.
Provenance validates and the detector found little synthetic evidence; factual review may still be needed.
Escalate for review rather than claiming the detector proved generation.
The credential may explicitly document AI-assisted creation or editing.
Investigate the credential failure without treating the detector as its explanation.
Editorial caution
Do not turn uncertainty into a binary public label
“No credentials” means no C2PA evidence was detected. A high AI score means a classifier found patterns of concern. Neither statement alone means “fake.”