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AI Detection for Redaction
Redaction is only as good as detection. Obscuring every face in a frame does not de-identify anyone if a distinctive tattoo is still visible, the street sign names the road, or the mobile data terminal shows the record. Redactor detects across all of it, and lets you tune how sure it has to be.
How Detection Works
Detection runs at upload through content processing, or on demand against an asset already in the library. You select the object and PII classes to look for and the duration to analyze, so part of a video can be processed on its own.
Detections are stored as timed data, not as marks on frames. That is what lets the studio show an object as a track across the timeline, and what makes a detection searchable and correctable rather than baked in.
What Detection Covers
PII in text and transcripts
Classes include person, age, date and time, identifiers, email, phone, URL, IP address, postcode, location, credit card, IBAN, cryptocurrency address, medical license, organization, profession and username, plus country-specific identifiers for the United States, United Kingdom, Spain, Italy, Poland, India, Australia and Singapore.
Custom entities
Organization-specific identifiers are defined three ways: a regular expression, a context-word rule, or a vocabulary list. A case number format that exists only in your agency is a definition, not a feature request.
Clinical text, clinical model
Medical narrative is de-identified with a RoBERTa model trained on the i2b2 clinical corpus, running as a recognizer alongside the general ones. The identifiers in a medical record take forms they do not take in a contract.
Correction
Detections are corrected before anything leaves: bounding boxes adjusted, duplicates merged, tracks split, objects renamed, false positives deleted, and missed objects added by hand. Automated detection proposes; a person disposes.
Objects in video and images
People
Persons, faces, heads, body parts
Vehicles
Car, bus, truck, bike, boat, airplane, train
License plates
Detected and read, so a plate is searchable as text rather than only as an object
Weapons
Firearms and other weapons
PPE
Including negative forms, so a missing item is detectable
Devices and screens
Monitors, displays, laptops, mobile phones, notebooks, and the mobile data terminal in a patrol vehicle
Identity documents
Detected as a class of their own
Traffic signs and signals
Street signs and house numbers
Environmental hazards
Scene-level hazard classes
Signatures
Handwritten signatures in documents and images
Tattoos
Identifying body art
Two of those deserve calling out, because they are the ones that quietly break a release.
Tattoos appear as a redaction class in the HIPAA and GDPR compliance profiles for a reason: obscuring a face while leaving a distinctive tattoo visible does not de-identify anyone.
Screens are named explicitly in the CJIS profile. A body-worn camera recording inside a patrol vehicle captures the terminal, and whatever record is displayed on it.
Tuning
| Control | Range | Default |
|---|---|---|
| Object detection threshold | 10 to 90 | 45 |
| Persistence before a detection counts | 3 to 30 consecutive frames | — |
| OCR thresholds | Set separately for video, image and document | — |
Precision and recall are a tuning decision, not a fixed property of the software. Redaction favors recall, because a missed detection is a disclosure. Investigation review often favors precision. Raising the persistence dial suppresses single-frame false positives at the cost of a few frames of latency on a genuine detection.
Test It on Your Own Footage
Send us a file with something difficult in it. Crowds, poor light, a screen in frame. We will show you what the detector returns and what the thresholds do to it.