Platform
Detection API
One API for the places fraud happens: send an image, a video or a recording and get a scored verdict back. SDKs for Node and Python, a CLI and an MCP server are published.
- One endpoint for images, video and audio
- Synchronous, with per-frame and per-window evidence
- Node and Python SDKs, CLI and MCP server — published
Simple to integrate
Submit media, receive a verdict, a score and the evidence behind it — per-frame for video, per-window for audio. Wire it into onboarding flows, support channels, claims pipelines, and content moderation.
Built for production
Synchronous answers at real-time latency for live flows; for volume work call the same endpoint in parallel within your key's per-minute limit, and a repeated file within 30 days is never charged twice. Every result carries the evidence your risk team and your auditors can read.
SDKs, CLI and MCP server
The REST API covers every language, and the official clients are published: @scam-ai/sdk for Node and TypeScript, scamai for Python, @scam-ai/cli for the terminal, and an MCP server — hosted or run locally — for AI agents.
Install lines, examples and the OpenAPI file are in the docs.
Running a proof of concept
Test on your own traffic — real genuine media plus known fraud — and score it blind. Then set the thresholds that decide what's auto-declined, reviewed, or approved.
- Approve55%
- Human review25%
- Auto-decline20%
/ROI
What this is worth
- One AI engine for every surface — add modules without adding vendors, contracts, or review queues.
- Evidence-backed results drop straight into your decisioning: auto-decline, step-up, or route to review.
- Live in a day: one REST API call in, a scored result out — no model training, no data-science team required.
Common questions
How do I integrate a deepfake detection API?
Send media over HTTPS and act on the JSON. Your backend submits an image, a video or a recording to the REST API with an API key and receives a verdict, a probability score and the evidence behind it — per-frame for video, per-window for audio. Your code compares the score to thresholds you set and branches — approve, step up, or route to review. There is no model to host; any language that can make an HTTP request can integrate today and the official Node and Python SDKs, the CLI and the MCP server are published.
What does a deepfake detection API return?
A scored, explainable result rather than a bare label. The JSON response carries a probabilistic score for manipulation, a verdict to branch on, and per-frame detail for video and per-window detail for audio — the evidence behind the score — so you can locate where in a clip manipulation appears. That structure serves machines and humans alike: your code thresholds on the score for automated decisions, while analysts and auditors read the signals to defend each outcome after the fact.
Does a deepfake detection API work in real time?
It should offer both speeds, because fraud workflows need both. Scam AI's synchronous endpoints return results at real-time latency for inline flows — an onboarding selfie check or a document step where a user is actively waiting. Volume work runs against the same synchronous endpoint in parallel, inside your key's per-minute limit; a file repeated within 30 days is not charged again. Both patterns return the same result structure, so one integration serves live decisions and pipeline jobs alike.
Ready to wire detection into your stack?
15 minutes, on your own media. Pick a slot and leave with a result.