SafeReel — Accuracy Report (2026-08-04)

Classifier: safereel-v1 — SafeReel's proprietary classification system, developed and operated in-house on our own GPU infrastructure, tuned and evaluated against real production traffic. Implementation details are not publicly disclosed — the numbers below, measured on the deployed system as a whole, are what we're accountable for.

Video verdicts are derived from analysis spanning the full duration of each file, not a fixed sample of its opening seconds.

Headline numbers

Set	n	Recall (porn)	FPR (clean)	Accuracy
Full golden set (2026-07-31)	1,089	92.2%	0.41%	95.5%
Pilot (human-reviewed labels)	100 (50/50)	96%	8%	94%
Video smoke set	24 (12/12)	100% (12/12)	0% (0/12)	100%

Human-confirmed buckets (the labels that matter most)

Bucket	safereel-v1
confirmed_porn recall (n=128)	96.1%
confirmed_clean FPR (n=11)	0%

An alternative recall-weighted configuration scores 95.3% recall / 9.1% FPR on the human-confirmed subset (n=139).

Methodology

Evaluation set: 1,089 scorable real-world videos (147GB) from production traffic. Results exclude files the API could not process (corrupt containers); dataset composition and known limitations are reported below.
Label quality: subsets differ in how strongly their labels are human-validated. Results are reported separately by label quality so readers can weight them appropriately.
Scoring: recall, false-positive rate, and accuracy using the definitions in this report. Raw per-item results are retained and available to customers under NDA on request.
What these numbers do not cover yet: borderline categories (swimwear, medical, artistic nudity) and deliberately adversarial content. Expanding the human-confirmed set is an ongoing effort — expect these numbers to tighten.

What we optimize for

False positives cost our customers real users. We track false-positive rate alongside recall on every accuracy update, and every reviewer-overturned false positive is treated as a defect with a review path.