Docs / AI Content Detection / AI-Generated Image Detection

AI-Generated Image Detection

genai

Detect if an image was generated by an AI model such as Nano Banana, Seedream, Stable Diffusion, GPT, MidJourney, Flux, Firefly and more.

This page describes AI image detection. AI video detection is also available.

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: Nano Banana, GPT-image, Stable Diffusion, MidJourney, Firefly, Flux, Reve, Recraft, Imagen, Ideogram, 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

Related model

The following 3 models can provide a useful complement to the AI-generated image detection model:

Deepfake Detection: Detect swapped or modified faces.
Type Detection: Detect if an image is a photography or an illustration.
AI Video Detection: Detect AI-generated videos.

Examples

AI-generated images

Stable Diffusion

MidJourney

Firefly

DALL-E 3

Ideogram

Flux

Kling

SeeDream

GPT

Nano Banana Pro

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);
});