REST API - Deeptrack

The deeptrack team recommends using the SDK for most integrations. The SDK handles authentication, file uploads, job polling, and error handling automatically. Consider checking if your preferred language is supported before using the raw API directly.View SDK Documentation →

Authentication

The deeptrack API authenticates via the X-API-Key header. Every request must include your API key.

X-API-Key: dt_your_key_here

To generate an API key, log into the deeptrack Dashboard and navigate to Settings → API Keys.

Keep your API key secure. Do not commit it to version control or expose it in client-side code.

Base URL

https://api.deeptrack.io

Size Limits

Media Type	Maximum Size
Image	15 MB
Video	500 MB
Audio	50 MB

Supported File Types

Type	Extensions
Image	`.jpg`, `.jpeg`, `.png`, `.webp`, `.gif`
Video	`.mp4`, `.mov`, `.avi`, `.mkv`
Audio	`.mp3`, `.wav`, `.m4a`, `.aac`, `.ogg`, `.flac`

Analyze an Image

Upload an image file and receive an authenticity verdict instantly. Endpoint

POST /v1/image/predict

Header	Value
`X-API-Key`	Your API key
`Content-Type`	`multipart/form-data`

Field	Type	Required	Description
`file`	file	Yes	Image file to analyze

import requests

BASE_URL = "https://api.deeptrack.io"
HEADERS  = {"X-API-Key": "dt_your_key_here"}

with open("photo.jpg", "rb") as f:
    resp = requests.post(
        f"{BASE_URL}/v1/image/predict",
        headers=HEADERS,
        files={"file": ("photo.jpg", f, "image/jpeg")},
    )

print(resp.json())

import fs from "fs";
import FormData from "form-data";

const BASE_URL = "https://api.deeptrack.io";
const HEADERS  = { "X-API-Key": "dt_your_key_here" };

const form = new FormData();
form.append("file", fs.createReadStream("photo.jpg"));

const resp = await fetch(`${BASE_URL}/v1/image/predict`, {
  method:  "POST",
  headers: { ...HEADERS, ...form.getHeaders() },
  body:    form,
});

console.log(await resp.json());

import fs from "fs";
import FormData from "form-data";

const BASE_URL = "https://api.deeptrack.io";
const HEADERS  = { "X-API-Key": "dt_your_key_here" };

interface ImageResult {
  filename:              string;
  prediction:            "Real" | "Fake" | "UNCERTAIN";
  confidence_percentage: number;
  raw_scores:            { Real: number; Fake: number };
}

const form = new FormData();
form.append("file", fs.createReadStream("photo.jpg"));

const resp = await fetch(`${BASE_URL}/v1/image/predict`, {
  method:  "POST",
  headers: { ...HEADERS, ...form.getHeaders() },
  body:    form as any,
});

const result = await resp.json() as ImageResult;
console.log(result);

curl -X POST https://api.deeptrack.io/v1/image/predict \
  -H "X-API-Key: dt_your_key_here" \
  -F "file=@photo.jpg"

Response

{
  "filename":               "photo.jpg",
  "prediction":             "FAKE",
  "confidence_percentage":  87.3,
  "raw_scores": {
    "Real": 12.7,
    "Fake": 87.3
  }
}

Field	Type	Description
`filename`	string	Name of the uploaded file
`prediction`	string	`Real`, `Fake`, or `UNCERTAIN`
`confidence_percentage`	float	Confidence of the verdict (0–100)
`raw_scores.Real`	float	Raw probability score for Real
`raw_scores.Fake`	float	Raw probability score for Fake

Analyze a Video

Video analysis is asynchronous. Upload the file, receive a job_id, then poll for the result.

Step 1 — Upload the video

Endpoint

POST /v1/video/predict/video

Header	Value
`X-API-Key`	Your API key
`Content-Type`	`multipart/form-data`

Field	Type	Required	Description
`file`	file	Yes	Video file to analyze

import requests
import time

BASE_URL = "https://api.deeptrack.io"
HEADERS  = {"X-API-Key": "dt_your_key_here"}

# step 1 — upload
with open("clip.mp4", "rb") as f:
    resp = requests.post(
        f"{BASE_URL}/v1/video/predict/video",
        headers=HEADERS,
        files={"file": ("clip.mp4", f, "video/mp4")},
    )

job_id = resp.json()["job_id"]
print(f"Job queued: {job_id}")

# step 2 — poll until done
while True:
    resp   = requests.get(f"{BASE_URL}/v1/video/jobs/{job_id}", headers=HEADERS)
    status = resp.json()["status"]
    print(f"Status: {status}")

    if status == "done":
        print(resp.json()["result"])
        break
    elif status == "error":
        print("Error:", resp.json()["error"])
        break

    time.sleep(3)

import fs from "fs";
import FormData from "form-data";

const BASE_URL = "https://api.deeptrack.io";
const HEADERS  = { "X-API-Key": "dt_your_key_here" };

// step 1 — upload
const form = new FormData();
form.append("file", fs.createReadStream("clip.mp4"));

const uploadResp = await fetch(`${BASE_URL}/v1/video/predict/video`, {
  method:  "POST",
  headers: { ...HEADERS, ...form.getHeaders() },
  body:    form,
});

const { job_id } = await uploadResp.json();
console.log("Job queued:", job_id);

// step 2 — poll until done
while (true) {
  await new Promise(r => setTimeout(r, 3000));

  const pollResp = await fetch(`${BASE_URL}/v1/video/jobs/${job_id}`, {
    headers: HEADERS,
  });
  const job = await pollResp.json();
  console.log("Status:", job.status);

  if (job.status === "done")  { console.log(job.result); break; }
  if (job.status === "error") { console.error(job.error); break; }
}

import fs from "fs";
import FormData from "form-data";

const BASE_URL = "https://api.deeptrack.io";
const HEADERS  = { "X-API-Key": "dt_your_key_here" };

interface VideoResult {
  label:        "REAL" | "FAKE" | "UNCERTAIN";
  confidence:   number;
  fake_prob:    number;
  face_pct:     number;
  total_frames: number;
  n_segments:   number;
  segments:     Array<{
    segment:    number;
    start_sec:  number;
    end_sec:    number;
    label:      string;
    confidence: number;
    fake_prob:  number;
  }>;
}

// step 1 — upload
const form = new FormData();
form.append("file", fs.createReadStream("clip.mp4"));

const uploadResp = await fetch(`${BASE_URL}/v1/video/predict/video`, {
  method:  "POST",
  headers: { ...HEADERS, ...form.getHeaders() },
  body:    form as any,
});

const { job_id } = await uploadResp.json() as { job_id: string };
console.log("Job queued:", job_id);

// step 2 — poll until done
while (true) {
  await new Promise(r => setTimeout(r, 3000));

  const pollResp = await fetch(`${BASE_URL}/v1/video/jobs/${job_id}`, {
    headers: HEADERS,
  });
  const job = await pollResp.json() as {
    status: string;
    result: VideoResult;
    error:  string;
  };
  console.log("Status:", job.status);

  if (job.status === "done")  { console.log(job.result); break; }
  if (job.status === "error") { throw new Error(job.error); }
}

# step 1 — upload
curl -X POST https://api.deeptrack.io/v1/video/predict/video \
  -H "X-API-Key: dt_your_key_here" \
  -F "file=@clip.mp4"

# step 2 — poll (replace JOB_ID with the returned job_id)
curl https://api.deeptrack.io/v1/video/jobs/JOB_ID \
  -H "X-API-Key: dt_your_key_here"

Upload Response

{
  "job_id":   "3f8a2c1d-...",
  "status":   "queued",
  "filename": "clip.mp4",
  "size_mb":  12.4,
  "poll_url": "/v1/video/jobs/3f8a2c1d-..."
}

Step 2 — Poll for Result

Endpoint

GET /v1/video/jobs/{job_id}

Poll this endpoint every 2–3 seconds until status is done or error. Larger files may take longer to analyze.

Poll Response

{
  "job_id":   "3f8a2c1d-...",
  "status":   "done",
  "filename": "clip.mp4",
  "result": {
    "label":        "FAKE",
    "confidence":   91.2,
    "fake_prob":    0.912,
    "face_pct":     98.0,
    "total_frames": 240,
    "n_segments":   4,
    "segments": [
      {
        "segment":    1,
        "start_sec":  0.0,
        "end_sec":    4.3,
        "label":      "FAKE",
        "confidence": 91.2,
        "fake_prob":  0.912
      }
    ]
  }
}

Field	Type	Description
`job_id`	string	Unique job identifier
`status`	string	`queued`, `processing`, `done`, or `error`
`result.label`	string	`REAL`, `FAKE`, or `UNCERTAIN`
`result.confidence`	float	Confidence percentage (0–100)
`result.fake_prob`	float	Raw fake probability (0.0–1.0)
`result.face_pct`	float	% of frames with a detected face
`result.total_frames`	int	Total frames processed
`result.n_segments`	int	Number of rPPG segments
`result.segments`	array	Per-segment breakdown

Check Usage

Returns your monthly usage stats for the authenticated API key. Endpoint

GET /v1/client/usage/me

resp = requests.get(f"{BASE_URL}/v1/client/usage/me", headers=HEADERS)
print(resp.json())

const resp = await fetch(`${BASE_URL}/v1/client/usage/me`, { headers: HEADERS });
console.log(await resp.json());

const resp = await fetch(`${BASE_URL}/v1/client/usage/me`, { headers: HEADERS });
console.log(await resp.json());

curl https://api.deeptrack.io/v1/client/usage/me \
  -H "X-API-Key: dt_your_key_here"

Response

{
  "owner":           "Gotham Media",
  "track":           "api",
  "plan":            "starter",
  "rate_per_scan":   0.39,
  "monthly_limit":   5000,
  "used_this_month": 23,
  "remaining":       4977,
  "resets_at":       "2026-03-31"
}

Error Reference

All errors return a JSON body with a detail field.

Status	Meaning	Example detail
`401`	Missing or invalid API key	`"Missing X-API-Key header."`
`400`	Bad request	`"Unsupported format '.wmv'."`
`413`	File too large	`"File too large (520MB). Max is 500MB."`
`422`	Missing required field	`"field required: file"`
`429`	Monthly limit exceeded	`"Monthly limit of 5000 scans exceeded."`
`503`	Model not loaded	`"Video model not loaded."`

{
  "detail": "Monthly limit of 5000 scans exceeded. Upgrade your plan at deeptrack.io/pricing"
}

Support

For questions or issues, email tech@deeptrack.io or reach out to your account manager.

​Authentication

​Base URL

​Size Limits

​Supported File Types

​Analyze an Image

​Response

​Analyze a Video

​Step 1 — Upload the video

​Upload Response

​Step 2 — Poll for Result

​Poll Response

​Check Usage

​Response

​Error Reference

​Support

Authentication

Base URL

Size Limits

Supported File Types

Analyze an Image

Response

Analyze a Video

Step 1 — Upload the video

Upload Response

Step 2 — Poll for Result

Poll Response

Check Usage

Response

Error Reference

Support