Live model status
The engine gets sharper with every scan.
HMNPROOF runs a self-training signal weighter on top of its detector stack. Every scan and every "this seems wrong" flag updates per-signal weights, so verdicts that were borderline yesterday become confident today.
Scans processed
0
Signals tracked
0
Model version
v1.0.0
Accuracy estimate
92.0%
Feedback signals
0
Last trained
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Top learned signals
Updated in real timeEach bar shows the learned weight of a signal — negative pushes a verdict toward authentic, positive pushes toward AI / manipulated.
No signals collected yet. Run a scan to start training.
How it learns
- 1. Observe. Every scan extracts signals from every layer of the stack — source (host, keywords, scheme), metadata (EXIF present, C2PA manifest), byte forensics (entropy band, JPEG DQT count, double-compression), AI vision verdict band, face-region deepfake band, opt-in identity match outcome, reverse-image availability, and NCII flags.
- 2. Track. The engine records how often each signal accompanied a high, moderate, or low verdict.
- 3. Correct. Every "this seems wrong" flag tells the engine when it was off — false positives push the signal toward authentic, false negatives push it toward AI.
- 4. Re-weight. Weights are smoothed by sample size, so a brand-new signal can't dominate until it has earned the data behind it.
- 5. Apply. Next scan, those weights nudge the score — capped so the base detectors always have the final say on extreme verdicts.