Media Classification
Overview
Identifying and classifying sexual content in seized media is central to child exploitation investigations. Manual review is slow, traumatic for investigators, and inconsistent at scale.
Rigr AI's Media Classification capability uses deep-learning object detection to identify sexual content, body parts, activities, and contextual indicators in images — then maps those detections to a structured severity classification aligned with established review frameworks.
Detection Capabilities
Our AI models detect visual elements across the following categories:
Body Parts & Anatomy
Genitalia, breasts, buttocks, hands, feet — each classified by developmental stage (infant through adult).
Sexual Activities
Intercourse, oral sex, penetration, masturbation, posing, and non-penetrative contact — detected and labelled precisely.
Age Demographics
Faces and full-body figures classified by developmental stage: infant, toddler, prepubescent, pubescent, and adult.
Contextual Indicators
Selfies (phone/camera), screenshots, CSAM network logos, clothing, jewellery, restraints, and other evidentiary markers.
Severity Classification
Every image is assigned a frame-level severity classification based on the most serious content detected. The classification maps to a structured scale designed for investigative triage:
| Severity | Classification | Description |
|---|---|---|
| 0 | No sexual content | Nothing of investigative interest detected |
| 2 | Exploitative / suggestive | Nudity, exposed anatomy, or sexualised context without explicit activity |
| 3 | Overt sexualised posing | Deliberate genital presentation or overtly sexualised positioning |
| 4 | Non-penetrative sexual activity | Masturbation, licking, or other non-penetrative sexual contact |
| 5 | Penetrative sexual activity | Intercourse, oral sex, anal or vaginal penetration |
Each detection is also enriched with contextual flags — such as Self-Generated, Sadomasochism, or CG Elements — providing additional investigative context.
Try It
Upload an image to see media classification in action. Adult content is accepted for testing purposes.
Adult content is fine for testing — do not upload CSAM.
Images you submit are kept for up to 7 days for trust & safety review, then deleted.
How we handle demo data →
Operational Use
Within investigative workflows, Media Classification is used to:
- Triage large volumes of seized media by severity
- Identify specific sexual activities, body parts, and contextual indicators
- Flag self-generated content, CSAM network logos, and other evidentiary markers
- Prioritise review queues so investigators focus on the most serious material first
The capability is also available as a standalone API or containerised application that can be deployed in an air-gapped environment.
Deployment and Control
- Fully containerised
- On-premise and air-gapped operation
- No data retention
- Customer retains full control of inputs and outputs
For Developers
The classification API accepts images via multipart upload and returns a severity classification, contextual flags, and per-object detections with bounding boxes and confidence scores.
POST /classify curl -X POST https://api.mes.rigr.ai/classify \
-H "X-API-KEY: $API_KEY" \
-F "[email protected]" \
-F "model=VisualyzeV2"{
"classification": {
"key": "rigr-penetrative",
"display_name": "Penetrative sexual activity",
"severity": 5
},
"flags": ["Self-Generated"],
"detections": [{
"class_name": "Male Receive Oral",
"score": 0.87,
"bbox": {"x": 0.12, "y": 0.45, "w": 0.31, "h": 0.62},
"ucs_sexual_content": "Penetrative Sexual Activity"
}]
}Frequently asked questions
What does Rigr AI's Media Classification detect?
Our AI models identify sexual content, body parts, activities, and contextual indicators in seized images, then map each image to a structured severity classification aligned with established review frameworks.
How is severity classified?
Every image is assigned a frame-level severity from 0 to 5 based on the most serious content detected, aligned with established review frameworks, with contextual flags such as self-generated content or CSAM-network markers.
How does it reduce investigator exposure?
Within VST Teams it triages thousands of seized files by severity and builds the priority queue, keeping the most serious material off reviewers' screens until it has to be seen. It is also available as a standalone API or containerised application.
Can it be deployed air-gapped?
Yes — fully containerised, on-premise and air-gapped, with no data retention and full customer control of inputs and outputs.