AI-Generated Image Detection
Table of contents
Overview
The AI-Generated Image Detection Model can help you determine if an image was entirely generated by an AI model, or if it is a real image. This model was trained on millions of artificially-created and human-created images spanning all sorts of content such as photography, art, drawings, memes and more.
The Model works by analyzing the visual (pixel) content of the image. 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 images generated by the main models currently in use: Stable Diffusion, Stable Diffusion XL, MidJourney, Dall-E, Adobe Firefly, GANs... Additional models will be added over time as they become available.
Use cases
- Tag AI-generated imagery as such, to limit the spread of misinformation and fake news
- Implement stricter moderation rules on AI-generated imagery
- Detect potential fraud with fake ids, fake profiles or fake claims
- Limit ai-generated spam
- Enact bans on AI-generated imagery
Examples
AI-generated images
Stable Diffusion
MidJourney 6
Firefly 3
DALL-E 3
StyleGan2 (thispersondoesnotexist)
Use the model
If you haven't already, create an account to get your own API keys.
Detect if an image was AI-generated
Let's say you want to check the following image:
You can either send the image URL, or upload the raw binary image.
Option 1: Send image URL
Here's how to proceed if you choose to share the image URL:
curl -X GET -G 'https://api.sightengine.com/1.0/check.json' \
-d 'models=genai' \
-d 'api_user={api_user}&api_secret={api_secret}' \
--data-urlencode 'url=https://sightengine.com/assets/img/examples/example-prop-c1.jpg'
# this example uses requests
import requests
import json
params = {
'url': 'https://sightengine.com/assets/img/examples/example-prop-c1.jpg',
'models': 'genai',
'api_user': '{api_user}',
'api_secret': '{api_secret}'
}
r = requests.get('https://api.sightengine.com/1.0/check.json', params=params)
output = json.loads(r.text)
$params = array(
'url' => 'https://sightengine.com/assets/img/examples/example-prop-c1.jpg',
'models' => 'genai',
'api_user' => '{api_user}',
'api_secret' => '{api_secret}',
);
// this example uses cURL
$ch = curl_init('https://api.sightengine.com/1.0/check.json?'.http_build_query($params));
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
$response = curl_exec($ch);
curl_close($ch);
$output = json_decode($response, true);
// this example uses axios
const axios = require('axios');
axios.get('https://api.sightengine.com/1.0/check.json', {
params: {
'url': 'https://sightengine.com/assets/img/examples/example-prop-c1.jpg',
'models': 'genai',
'api_user': '{api_user}',
'api_secret': '{api_secret}',
}
})
.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 |
| url | string | URL of the image to analyze |
| models | string | comma-separated list of models to apply |
| api_user | string | your API user id |
| api_secret | string | your API secret |
Option 2: Send raw image
Here's how to proceed if you choose to upload the raw image:
curl -X POST 'https://api.sightengine.com/1.0/check.json' \
-F 'media=@/path/to/image.jpg' \
-F 'models=genai' \
-F 'api_user={api_user}' \
-F 'api_secret={api_secret}'
# this example uses requests
import requests
import json
params = {
'models': 'genai',
'api_user': '{api_user}',
'api_secret': '{api_secret}'
}
files = {'media': open('/path/to/image.jpg', 'rb')}
r = requests.post('https://api.sightengine.com/1.0/check.json', files=files, data=params)
output = json.loads(r.text)
$params = array(
'media' => new CurlFile('/path/to/image.jpg'),
'models' => 'genai',
'api_user' => '{api_user}',
'api_secret' => '{api_secret}',
);
// this example uses cURL
$ch = curl_init('https://api.sightengine.com/1.0/check.json');
curl_setopt($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/image.jpg'));
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/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 |
| models | string | comma-separated list of models to apply |
| api_user | string | your API user id |
| api_secret | string | your API secret |
Response
The API will then return a JSON response with the ai_generated score. This score is a float between 0 and 1. The higher the value, the higher the confidence that the image is AI-generated:
{
"status": "success",
"request": {
"id": "req_0zrbHDeitGYY7wEGncAne",
"timestamp": 1491402308.4762,
"operations": 1
},
"type": {
"ai_generated": 0.01
},
"media": {
"id": "med_0zrbk8nlp4vwI5WxIqQ4u",
"uri": "https://sightengine.com/assets/img/examples/example-prop-c2.jpg"
}
}
Any other needs?
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...
Was this page helpful?