Models / Offensive Content Detection

Offensive Content Detection

Overview

The offensive model is useful to determine if an image or a video contains offensive content or hate content. In addition to detecting such content, this model gives details on the position and the type of the offensive content found.

The offensive content detected falls broadly into the following categories:

Nazi era symbols
Confederate symbols
Supremacist symbols
Offensive gestures
Terrorist symbols

See the concept section for the full list of detected concepts.

Concepts

This section lists the concepts detected by the Offensive Detection Model.

Nazi-era symbols

Nazi hakenkreuz

Nazi symbol that can be found on objects from the nazi era, costumes, drawings, cartoons, tattoos...

nazi

Nazi flags

Flags from the nazi era, such as the nazi party flag, the Reichskriegsflagge, the Reichsdienstflagge...

nazi

Iron cross

Military decoration of Prussia, later used in Nazi Germany. Present on nazi flags, costumes, medals and other objects.

nazi

SS bolts

Schutzstaffel sigrunes. They symbolised victory and are present on nazi era objects such as helmets, medals, costumes...

nazi

Sonnenrad

Sonnenrad or sunwheel. Ancient European symbol appropriated by the Nazis.

nazi

Sturmabteilung

Emblem of Hitler's SA troops. Some neo-nazis and white supremacists currently use this symbol.

nazi

Supremacist symbols

KKK

Ku klux klan

supremacist

Burning cross

Cross burning is a practice associated with the Ku Klux Klan.

supremacist

Blood drop cross

Primary symbol associated to the Ku Klux Klan groups.

supremacist

Celtic cross

There are many types of celtic crosses, with different meanings. The one depicted is one of the most commonly used white supremacist symbols.

supremacist

Valknut

Valknut or valknot. Old Norse symbol appropriated by some white supremacists.

supremacist

Odal rune

Odal rune, also known as othala rune. Originally part of the runic alphabet system but appropriated by the Nazis as a symbol for aryanism

supremacist

Wolfsangel

Old runic symbol appropriated during the Nazi era. It has become a symbol of choice for neo-Nazis

supremacist

Confederate symbols

Confederate flag

Flag of the Confederate States of America

confederate

Gestures

Middle finger

Obscene hand gesture in western culture

middle_finger

Other symbols

ISIS flag

IS / ISIL / ISIS / Daesh version of the Black Standard

terrorist

Principles

The Offensive detection does not use any image meta-data to determine the presence of a offensive content on an image. The file extension, the meta-data or the name will not influence the result. The classification is made using only the pixel content of the image or video.

On most sites and apps, images containing offensive content will be systematically removed.

Offensive Detection works with black and white images as well as color images or images with filters.

Use-cases

Block or detect users who submit images or videos containing offensive content
Hide, Blur or Filter hateful symbols and references in images and videos
Protect your users from unwanted content

Limitations

Elements smaller than 5% of the width or height of the image may not be detected.

Recommended threshold

When processing the "offensive" value returned by the API, users generally set a threshold. Images or videos with a value above this threshold will be flagged as potentially containing offensive content while images or videos with a value below will be considered to be safe.

Thresholds need to be fine-tuned for each individual use-case. Depending on your tolerance to false positives or false negatives, the threshold should be adapted.

If you want to reduce false negatives, you may want to start with a threshold of 0.5 (meaning that images with an "alcohol" value above 0.5 would be flagged)

If you want to reduce false positives, you may want to start with a threshold of 0.8

If there are some concepts that you want to allow, or if you want to set different thresholds for different concepts, this is also something you can do, as the API will return a score for each object found.

Details

Along with the offensive probability, the model will return a list of all the offensive elements found in the image (if any), along with their positions and type.

Use the model (images)

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

Detect hate & offensive content

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=offensive' \
    -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': 'offensive',
  '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' => 'offensive',
  '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': 'offensive',
    '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
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

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=offensive' \
    -F 'api_user={api_user}' \
    -F 'api_secret={api_secret}'


# this example uses requests
import requests
import json

params = {
  'models': 'offensive',
  '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' => 'offensive',
  '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', 'offensive');
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

API response

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

                  
                  
{
  "status": "success",
  "request": {
      "id": "req_24DNGegGf1Mo0n4rpaRwZ",
      "timestamp": 1512898748.652,
      "operations": 1
  },
  "offensive": {
      "prob": 0.01,
      "nazi": 0.01,
      "confederate": 0.01,
      "supremacist": 0.01,
      "terrorist": 0.01,
      "middle_finger": 0.01,
      "boxes": []
  },
  "media": {
      "id": "med_24DNJfN2BlCGPGQBoZ5dO",
      "uri": "https://sightengine.com/assets/img/examples/example-fac-1000.jpg"
  }
}

Use model (Videos)

Detecting Hate & Offensive content 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=offensive' \
  -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': 'offensive',
  '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' => 'offensive',
  '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', 'offensive');
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

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=offensive' \
  -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': 'offensive',
  # 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' => 'offensive',
  // 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', 'offensive');
// 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

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=offensive' \
    -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('offensive').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(['offensive'])->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(['offensive']).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

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
      },
      "offensive": {
        "prob": 0.01,
        "nazi": 0.01,
        "confederate": 0.01,
        "supremacist": 0.01,
        "terrorist": 0.01,
        "middle_finger": 0.01,
        "boxes": []
      },
     },
     ...
    ]
  },
  "media": {
    "id": "med_gmgHcUOwe41rWmqwPhVNU",
    "uri": "yourfile.mp4"
  },
}

You can use the classes under the offensive object to detect hateful or offensive content 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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REFERENCE

Offensive Content Detection

Table of contents

Overview

Concepts

Nazi-era symbols

Nazi hakenkreuz

Nazi flags

Iron cross

SS bolts

Sonnenrad

Sturmabteilung

Supremacist symbols

KKK

Burning cross

Blood drop cross

Celtic cross

Valknut

Odal rune

Wolfsangel

Confederate symbols

Confederate flag

Gestures

Middle finger

Other symbols

ISIS flag

Principles

Use-cases

Limitations

Recommended threshold

Details

Use the model (images)

Detect hate & offensive content

Option 1: Send image URL

Option 2: Send raw image

API response

Use model (Videos)

Detecting Hate & Offensive content in videos

Option 1: Short video

Option 2: Long video

Option 3: Live-stream

Moderation result

Any other needs?