Predicting a synthesised image

You can use this endpoint to detect if an image has been artificially generated.

Once an image has been submitted, our machine learning model will predict the likelihood of the provided image being artificially generated by other models such as Stable Diffusion or Midjourney.

In our testing, each model performed at >80% accuracy in detecting whether an image was a deepfake within their category. The following models are available for use:

Model Name	Accuracy	Description
Personr_DF_5M	0.923	Trained on a size of 5M parameters (Stable Diffusion v1.4, v1.5, v2.1), and designed to detect Stable Diffusion-generated images.
Personr_DF_200M	0.950	Trained on a size of 200M parameters (Stable Diffusion v1.4, v1.5, v2.1), and designed to detect Stable Diffusion-generated images.
Personr_MJ_5M	0.849	Trained on a size of 5M parameters (Midjourney v4, v5.1, v5.2), and designed to detect Midjourney-generated images.
Personr_MJ_200M	0.902	Trained on a size of 200M parameters (Midjourney v4, v5.1, v5.2), and designed to detect Midjourney-generated images.
Personr_Deepfake_v5	0.959	Our most accurate model, trained on 200M parameters using a combination of Stable Diffusion and Midjourney-generated images.

Supported file formats include: .PNG, .JPEG & .JPG

Extremely bright or vibrant images may affect accuracy during prediction. Additionally, predictions are indications and shouldn't be considered a definitive answer.

Predicting a synthesised image

POST /api-deepfake-detection

Detect whether an image has been artificially generated.

Headers

Name	Value
Content-Type	application/json
Authorization	Bearer YOUR_TOKEN

Body

Name	Type	Description
imageUrl	string	The URL of the image, including file extension (required)
modelName	string	The name of the model you want to use for prediction, shown above (required)
token	string	The token provided to you during onboarding. This is separate from your Bearer token and exclusive to this endpoint (required)

Request

cURL

curl -X POST \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -d '{
    "imageUrl": "YOUR_IMAGE_URL/image.jpg",
    "modelName": "MODEL_NAME",
    "token": "YOUR_ENDPOINT_TOKEN"
}' \
  https://enterprise.personr.co/api/1.1/wf/api-deepfake-detection

Node.js

const axios = require('axios');

const url = 'https://enterprise.personr.co/api/1.1/wf/api-deepfake-detection';
const token = 'YOUR_BEARER_TOKEN';
const endpointToken = 'YOUR_ENDPOINT_TOKEN';

const data = {
    imageUrl: 'YOUR_IMAGE_URL/image.jpg',
    modelName: 'Personr_DF_5M',
    token: endpointToken
};

const config = {
    headers: {
        'Content-Type': 'application/json',
        'Authorization': `Bearer ${token}`
    }
};

axios.post(url, data, config)
    .then(response => {
        console.log(response.data);
    })
    .catch(error => {
        console.error('Error:', error);
    });

Ruby

require 'net/http'
require 'uri'
require 'json'

url = URI.parse('https://enterprise.personr.co/api/1.1/wf/api-deepfake-detection')
token = 'YOUR_BEARER_TOKEN'
endpoint_token = 'YOUR_ENDPOINT_TOKEN'

data = {
  imageUrl: 'YOUR_IMAGE_URL/image.jpg',
  modelName: 'Personr_DF_5M',
  token: endpoint_token
}

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url.path)
request['Content-Type'] = 'application/json'
request['Authorization'] = "Bearer #{token}"
request.body = data.to_json

response = http.request(request)

puts response.body

Python

import requests

url = 'https://enterprise.personr.co/api/1.1/wf/api-deepfake-detection'
token = 'YOUR_BEARER_TOKEN'
endpoint_token = 'YOUR_ENDPOINT_TOKEN'

data = {
    'imageUrl': 'hYOUR_IMAGE_URL/image.jpg',
    'modelName': 'Personr_DF_5M',
    'token': endpoint_token
}

headers = {
    'Content-Type': 'application/json',
    'Authorization': 'Bearer {}'.format(token)
}

response = requests.post(url, json=data, headers=headers)

if response.status_code == 200:
    print(response.json())
else:
    print('Error:', response.text)

Response

200

{
    "status": "success",
    "response": {
        "result": "Artificial",
        "labels": [
            "Artificial",
            "Genuine"
        ],
        "confidence": [
            0.875041127204895,
            0.12495886534452438
        ]
    }
}

400

{
    "statusCode": 400,
    "message": "Error: incorrect modelName provided"
}

401

{
    "error_class": "Unauthorized",
    "args": {
        "code": "1709700303639x0000000000000"
    },
    "message": null,
    "translation": "Invalid or expired token: ABC123"