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        Models / Self-harm Detection

        Self-harm Detection BETA

        Table of contents

        Self-harm classes

        The Self-harm model is useful to detect images and videos of intentional self-inflicted injuries along with other indicators of self-harm such as firearms pointed at own body or wrist scars.

        Injuries with no evidence of self-harm, drug use, torture, people handling knives with no evidence of intent of self-harm are all ignored by this model.

        The model returns an overall probability in self-harm.prob. For more fine-grained decisions, you can use the following classes:

        • Real self-harm: Photos displaying or suggesting real self-harm
          self-harm.type.real
          • People cutting themselves
          • Person pointing a firearm at their own head or chest
          • Person holding a knife or razor blade close to their body
          • Displays of past self-harm attempts, such as scars or burns, where the location, number, direction indicate that they were self-inflicted.
          • Self-flagellation
          • Person willingly putting themselves in a situation with immediate danger to their life, such as lying down on train tracks or jumping from the top of a building
        • Fake self-harm: Photos displaying or suggesting faked self-harm
          self-harm.type.fake
          • Person pointing a toy weapon at their own head or chest
          • Person mimicking a gun with their hand pointed at their own head
          • Tattoos, fake blood and other displays used to suggest self-harm while being obviously fake
        • Animated self-harm: Illustrations and animations displaying or suggesting self-harm
          self-harm.type.animated
          • Illustrations of characters cutting themselves
          • Illustrations of characters pointing a firearm at their own head or chest
          • Illustrations of characters holding a knife or razor blade close to their body

        Related models

        The following 3 models can provide a useful complement to the self-harm model:

        Use the model (images)

        If you haven't already, create an account to get your own API keys.

        Detect self-harm in images

        Let's say you want to moderate the following image:

        You can either share a URL to the image, 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=self-harm' \
    -d 'api_user={api_user}&api_secret={api_secret}' \
    --data-urlencode 'url=https://sightengine.com/assets/img/examples/example-fac-1000.jpg'


        
# this example uses requests
import requests
import json

params = {
  'url': 'https://sightengine.com/assets/img/examples/example-fac-1000.jpg',
  'models': 'self-harm',
  '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-fac-1000.jpg',
  'models' => 'self-harm',
  '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-fac-1000.jpg',
    'models': 'self-harm',
    '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

        ParameterTypeDescription
        mediabinaryimage to analyze
        modelsstringcomma-separated list of models to apply
        api_userstringyour API user id
        api_secretstringyour 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=self-harm' \
    -F 'api_user={api_user}' \
    -F 'api_secret={api_secret}'


        
# this example uses requests
import requests
import json

params = {
  'models': 'self-harm',
  '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' => 'self-harm',
  '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', 'self-harm');
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

        ParameterTypeDescription
        mediabinaryimage to analyze
        modelsstringcomma-separated list of models to apply
        api_userstringyour API user id
        api_secretstringyour API secret

        API response

        The API will then return a JSON response with the following structure:

                          
                  
{
    "status": "success",
    "request": {
        "id": "req_gcTp4s63IAAni0lFOT7KK",
        "timestamp": 1714997478.552115,
        "operations": 1
    },
    "self-harm": {
        "prob": 0.01,
        "type": {
            "real": 0.01,
            "fake": 0.01,
            "animated": 0.01
        }
    },
    "media": {
        "id": "med_gcTpqyOZ18IMsiMe4Ar28",
        "uri": "https://sightengine.com/img/examples/example-fac-1000.jpg"
    }
}

        Use model (Videos)

        Detecting self-harm in videos

        Option 1: Short video

        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=self-harm' \
  -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': 'self-harm',
  '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' => 'self-harm',
  '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', 'self-harm');
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

        ParameterTypeDescription
        mediabinaryimage to analyze
        modelsstringcomma-separated list of models to apply
        intervalfloatframe interval in seconds, out of 0.5, 1, 2, 3, 4, 5 (optional)
        api_userstringyour API user id
        api_secretstringyour API secret

        Option 2: Long video

        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=self-harm' \
  -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': 'self-harm',
  # 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' => 'self-harm',
  // 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', 'self-harm');
// 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

        ParameterTypeDescription
        mediabinaryimage to analyze
        callback_urlstringcallback URL to receive moderation updates (optional)
        modelsstringcomma-separated list of models to apply
        intervalfloatframe interval in seconds, out of 0.5, 1, 2, 3, 4, 5 (optional)
        api_userstringyour API user id
        api_secretstringyour API secret

        Option 3: Live-stream

        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=self-harm' \
    -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('self-harm').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(['self-harm'])->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(['self-harm']).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

        ParameterTypeDescription
        stream_urlstringURL of the video stream
        callback_urlstringcallback URL to receive moderation updates (optional)
        modelsstringcomma-separated list of models to apply
        intervalfloatframe interval in seconds, out of 0.5, 1, 2, 3, 4, 5 (optional)
        api_userstringyour API user id
        api_secretstringyour API secret

        Moderation result

        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
      },
      "self-harm": {
        "prob": 0.01,
        "type": {
          "real": 0.01,
          "fake": 0.01,
          "animated": 0.01
        }
      },
     },
     ...
    ]
  },
  "media": {
    "id": "med_gmgHcUOwe41rWmqwPhVNU",
    "uri": "yourfile.mp4"
  },
}

        You can use the classes under the self-harm object to detect self-harm in the video.

        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...

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