Short answer
Yes. AI image detectors can produce both false positives and false negatives, which is why a single score should never be treated as final proof.
Why the score can be wrong
Detector outputs are probabilistic. Compression, screenshots, editing history, missing metadata, and unusual but legitimate image features can all affect confidence.
What a better review workflow looks like
- check whether a trusted provenance record exists
- inspect EXIF or related metadata when it is available
- compare detector signals instead of trusting only one layer
- review visible inconsistencies and context
- escalate ambiguous cases to a human reviewer
What teams should do in practice
If a detector says an image looks suspicious, the next step should not be "declare it fake." The safer next step is to ask why it looks suspicious and whether provenance, metadata, and visible evidence support the same conclusion.
For that reason, teams should treat detector scores as triage inputs rather than truth machines. A good workflow helps reviewers decide whether to publish, label, hold, or escalate the image.
Where ImageVerity fits
ImageVerity is designed to combine provenance, metadata, detector signals, and visual reasoning in one workflow so the reviewer can understand the evidence chain instead of only seeing a single number.
If you want to see how that works, review the methodology, inspect sample reports, or run a free review.