Image moderation

Input

Submit a multipart/form-data request with:

Field	Type	Required	Description
`image`	string	Yes	Publicly reachable HTTP or HTTPS URL of the image to scan. The classifiers fetch it directly.
`webhook_url`	string	No	URL to receive the result on completion.

curl -X POST https://api.omnifence.ai/api/v1/moderate/image \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -F "image=https://example.com/photo.jpg"

The image field must be a public HTTP or HTTPS URL. URLs using another scheme, or resolving to a private or internal network address, are rejected with 400 INVALID_REQUEST.

Pipeline

Checks run in parallel on the submitted image:

VLM classification

A vision-language model analyses the image for each configured visual moderation category — by default NSFW, AI adult general, and underage. Disabled categories are skipped at enqueue time.

Custom VLM categories

If your client has any custom categories enabled, each one runs as an additional VLM child job in parallel. Results are returned nested under categories.custom on the completed job.

Output

When all child checks complete, the parent job merges the results:

{
  "is_prohibited": false,
  "categories": {
    "ai_adult_general": false,
    "underage": false,
    "nsfw": false,
    "custom": {
      "brand_logos": false
    }
  },
  "job_id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "type": "image",
  "status": "completed",
  "created_at": "2026-05-19T12:00:00.000Z",
  "completed_at": "2026-05-19T12:00:05.000Z"
}

categories.custom is only included when at least one custom category ran for the job. If you haven’t configured any custom categories, the custom key is omitted entirely. See decision logic for how the prohibition decision is determined.

Getting started

Platform

Moderation

Input

Pipeline

VLM classification

Custom VLM categories

Output

​Input

​Pipeline

​VLM classification

​Custom VLM categories

​Output

Input

Pipeline

VLM classification

Custom VLM categories

Output