The AI-Generated Video Detection Model can help you determine if a video was entirely generated by an AI model, or if it is a real video. This model was trained on millions of artificially-created and human-created videos spanning all sorts of content such as real life, art, cartoons and more.
The Model works by analyzing the visual (pixel) content of the video. No meta-data is used in the analysis. Tampering with meta-data such as EXIF data therefore has no effect on the scoring.
The Model was trained to detect videos created by the main generators currently in use: Veo, Sora, Runway, Pika, MidJourney, Kling... Additional generators will be added over time as they become available.
For AI-image detection, you can use the AI-image detection model.
If you haven't already, create an account to get your own API keys.
Here's how to proceed to analyze a short video (less than 1 minute):
curl -X POST 'https://api.sightengine.com/1.0/video/check-sync.json' \
-F 'media=@/path/to/video.mp4' \
-F 'models=genai' \
-F 'api_user={api_user}' \
-F 'api_secret={api_secret}'
# this example uses requests
import requests
import json
params = {
# specify the models you want to apply
'models': 'genai',
'api_user': '{api_user}',
'api_secret': '{api_secret}'
}
files = {'media': open('/path/to/video.mp4', 'rb')}
r = requests.post('https://api.sightengine.com/1.0/video/check-sync.json', files=files, data=params)
output = json.loads(r.text)
$params = array(
'media' => new CurlFile('/path/to/video.mp4'),
// specify the models you want to apply
'models' => 'genai',
'api_user' => '{api_user}',
'api_secret' => '{api_secret}',
);
// this example uses cURL
$ch = curl_init('https://api.sightengine.com/1.0/video/check-sync.json');
curl_setopt($ch, CURLOPT_POST, true);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
curl_setopt($ch, CURLOPT_POSTFIELDS, $params);
$response = curl_exec($ch);
curl_close($ch);
$output = json_decode($response, true);
// this example uses axios and form-data
const axios = require('axios');
const FormData = require('form-data');
const fs = require('fs');
data = new FormData();
data.append('media', fs.createReadStream('/path/to/video.mp4'));
// specify the models you want to apply
data.append('models', 'genai');
data.append('api_user', '{api_user}');
data.append('api_secret', '{api_secret}');
axios({
method: 'post',
url:'https://api.sightengine.com/1.0/video/check-sync.json',
data: data,
headers: data.getHeaders()
})
.then(function (response) {
// on success: handle response
console.log(response.data);
})
.catch(function (error) {
// handle error
if (error.response) console.log(error.response.data);
else console.log(error.message);
});
See request parameter description
| Parameter | Type | Description |
| media | binary | image to analyze |
| models | string | comma-separated list of models to apply |
| interval | float | frame interval in seconds, out of 0.5, 1, 2, 3, 4, 5 (optional) |
| api_user | string | your API user id |
| api_secret | string | your API secret |
Here's how to proceed to analyze a long video. Note that if the video file is very large, you might first need to upload it through the Upload API.
curl -X POST 'https://api.sightengine.com/1.0/video/check.json' \
-F 'media=@/path/to/video.mp4' \
-F 'models=genai' \
-F 'callback_url=https://yourcallback/path' \
-F 'api_user={api_user}' \
-F 'api_secret={api_secret}'
# this example uses requests
import requests
import json
params = {
# specify the models you want to apply
'models': 'genai',
# specify where you want to receive result callbacks
'callback_url': 'https://yourcallback/path',
'api_user': '{api_user}',
'api_secret': '{api_secret}'
}
files = {'media': open('/path/to/video.mp4', 'rb')}
r = requests.post('https://api.sightengine.com/1.0/video/check.json', files=files, data=params)
output = json.loads(r.text)
$params = array(
'media' => new CurlFile('/path/to/video.mp4'),
// specify the models you want to apply
'models' => 'genai',
// specify where you want to receive result callbacks
'callback_url' => 'https://yourcallback/path',
'api_user' => '{api_user}',
'api_secret' => '{api_secret}',
);
// this example uses cURL
$ch = curl_init('https://api.sightengine.com/1.0/video/check.json');
curl_setopt($ch, CURLOPT_POST, true);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
curl_setopt($ch, CURLOPT_POSTFIELDS, $params);
$response = curl_exec($ch);
curl_close($ch);
$output = json_decode($response, true);
// this example uses axios and form-data
const axios = require('axios');
const FormData = require('form-data');
const fs = require('fs');
data = new FormData();
data.append('media', fs.createReadStream('/path/to/video.mp4'));
// specify the models you want to apply
data.append('models', 'genai');
// specify where you want to receive result callbacks
data.append('callback_url', 'https://yourcallback/path');
data.append('api_user', '{api_user}');
data.append('api_secret', '{api_secret}');
axios({
method: 'post',
url:'https://api.sightengine.com/1.0/video/check.json',
data: data,
headers: data.getHeaders()
})
.then(function (response) {
// on success: handle response
console.log(response.data);
})
.catch(function (error) {
// handle error
if (error.response) console.log(error.response.data);
else console.log(error.message);
});
See request parameter description
| Parameter | Type | Description |
| media | binary | image to analyze |
| callback_url | string | callback URL to receive moderation updates (optional) |
| models | string | comma-separated list of models to apply |
| interval | float | frame interval in seconds, out of 0.5, 1, 2, 3, 4, 5 (optional) |
| api_user | string | your API user id |
| api_secret | string | your API secret |
Here's how to proceed to analyze a live-stream:
curl -X GET -G 'https://api.sightengine.com/1.0/video/check.json' \
--data-urlencode 'stream_url=https://domain.tld/path/video.m3u8' \
-d 'models=genai' \
-d 'callback_url=https://your.callback.url/path' \
-d 'api_user={api_user}' \
-d 'api_secret={api_secret}'
# if you haven't already, install the SDK with 'pip install sightengine'
from sightengine.client import SightengineClient
client = SightengineClient('{api_user}','{api_secret}')
output = client.check('genai').video('https://domain.tld/path/video.m3u8', 'https://your.callback.url/path')
// if you haven't already, install the SDK with 'composer require sightengine/client-php'
use \Sightengine\SightengineClient;
$client = new SightengineClient('{api_user}','{api_secret}');
$output = $client->check(['genai'])->video('https://domain.tld/path/video.m3u8', 'https://your.callback.url/path');
// if you haven't already, install the SDK with 'npm install sightengine --save'
var sightengine = require('sightengine')('{api_user}', '{api_secret}');
sightengine.check(['genai']).video('https://domain.tld/path/video.m3u8', 'https://your.callback.url/path').then(function(result) {
// The API response (result)
}).catch(function(err) {
// Handle error
});
See request parameter description
| Parameter | Type | Description |
| stream_url | string | URL of the video stream |
| callback_url | string | callback URL to receive moderation updates (optional) |
| models | string | comma-separated list of models to apply |
| interval | float | frame interval in seconds, out of 0.5, 1, 2, 3, 4, 5 (optional) |
| api_user | string | your API user id |
| api_secret | string | your API secret |
The Moderation result will be provided either directly in the request response (for sync calls, see below) or through the callback URL your provided (for async calls).
Here is the structure of the JSON response with moderation results for each analyzed frame under the data.frames array:
{
"status": "success",
"request": {
"id": "req_gmgHNy8oP6nvXYaJVLq9n",
"timestamp": 1717159864.348989,
"operations": 21
},
"data": {
"frames": [
{
"info": {
"id": "med_gmgHcUOwe41rWmqwPhVNU_1",
"position": 0
},
"type": {
"ai_generated": 0.99,
}
},
...
]
},
"media": {
"id": "med_gmgHcUOwe41rWmqwPhVNU",
"uri": "yourfile.mp4"
},
}
You can use the classes under the type object to detect AI-generated parts in the video.
See our full list of Image/Video models for details on other filters and checks you can run on your images and videos. You might also want to check our Text models to moderate text-based content: messages, reviews, comments, usernames...
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