Models / Text Moderation in Images/Videos

Text Moderation in Images/Videos

Overview

The Visual Text Moderation API is useful to determine if an image or video contains unwanted text such as profanity or personally identifiable information.

The API gives you fine-grained control over the moderation decision. The API will tell you what type of content has been found (phone number, email address, discriminatory content, sexual content...) along with a text extract of the content. You can then use this response to reject, flag or review the image/video on your end.

Just like our other Image Moderation APIs, this API uses advanced AI to perform the analysis entirely automatically. There are no humans reviewing your content. This helps us achieve very fast turnaround times — typically a few hundreds of milliseconds — and very high scalablity.

boxes showing natural text found on a woman's shirt and artificial text added through post-processing

Image containing flagged profanity

Principles

The Text Moderation API for Images works in several steps:

Detection of text items contained in the image
Recognition of the text (this is equivalent to transforming text into string objects)
Analysis of the recognized text, through our rule-based text moderation engine

Use-cases

Prevent users from adding insults, profanity, racial slurs or sexually suggestive text in an image
Remove photos that contain PII such as an email address or phone number
Flag users who include links/URLs in their images
Prevent users from mentioning their social accounts in images

Use the OCR API to access the raw text content of the image or video.

Profanity Detection in Images

Profanity Detection will enable you to detect insults, discriminatory content, sexual content or other inappropriate words and phrases in your images.

It is a lot stronger than word-based filters. It uses advanced language analysis to detect objectionable content, even when users specifically attempt to circumvent your filters. It covers obfuscation techniques such as repetitions, insertions, spelling mistakes, leet speak and more. Learn more on our Text Moderation Engine.

Image containing flagged profanity

Personal Information Detection in Images

Email addresses

Email addresses will be detected and flagged as such in the image.

Image with a flagged email address

Phone numbers

Phone numbers will be detected and flagged as such in the image.

You can select the countries to be covered through the opt_countries parameter. Provide a comma-separated list of the ISO 3166 2-letter country codes. For instance us for the United-States, fr for France. See the full list of supported countries.

If you do not specify any country, the API will default to the following list of countries: United States us, France fr, United Kingdom gb

Image with a flagged US phone number

Link and URL Detection in Images

Links and URLs will be detected and flagged as such in the image.

Image with a flagged link to a twitter handle

In addition to detecting URLs in Images or Videos, you can also moderate the link to determine if the link is unsafe, deceptive or known to contain otherwise unwanted content (such as adult content, gambling, drugs...). More details are available on the URL and link moderation page.

Social Account Detection in Images

Mentions of social networks and social accounts can be detected in images. This detection works on any text-based mention. It will not flag logos of said social networks.

The most common social networks are supported by default: facebook, whatsapp, snapchat. Other social networks can be made available on a custom basis. Reach out for more.

Image with a flagged mention of a social network or account

Custom disallow or allow lists

You can use custom disallow lists (also known as blacklist or ban list) and allow lists along with with Text-in-image moderation and Text-in-video moderation.

To create a new text list, go to the dedicated page on your dashboard.

Once your text list has been created, you will have the opportunity to add entries to the text list.

View your custom list

Each entry is a text item that you want to detect, along with meta-data to help our engine determine how and when this entry should be detected. The following information will be needed:

Text: this is the text item you want to detect.
Language: this is the language to which the text item applies. If it appears in a text item with a different language, it will not be flagged. If you need a text item to be detected in all languages, select all
Match type: this is the way the detection engine should detect. Choose exact match if you want the engine to detect the exact string, with no modifications (apart from case modifications).
Choose standard match if you want the engine to detect variations such as phonetic variations, repetitions, typos, leet speak etc... This will typically cover millions of variations and make sure your users cannot simply obfuscate the word to fool the engine.
Category: this is a suggested categorization of the text item, to help you sort and filter entries.

Add a new entry to your custom list

While most users only need to create a single text list, you can create multiple text lists to apply different moderation criteria to different types of text items.

In order to use a custom list, you will have to specify the corresponding id of the list in the opt_textlist parameter in your API calls.

Languages

English is the default language used for the text recognition and profanity filtering.

You can set a different language with the opt_lang parameter. To do so use the following codes:

Language	Code
English (default)	en
Chinese	zh
Danish	da
Dutch	nl
Finnish	fi
French	fr
German	de
Italian	it
Norwegian	no
Polish	pl
Portuguese	pt
Spanish	es
Swedish	sv
Tagalog / Filipino	tl
Turkish	tr

Other languages are available upon request. Please get in touch.

Use the model (images)

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

Moderate text 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=text-content' \
    -d 'api_user={api_user}&api_secret={api_secret}' \
    --data-urlencode 'url=https://sightengine.com/assets/img/examples/example-text-ocr-3.jpg'


# this example uses requests
import requests
import json

params = {
  'url': 'https://sightengine.com/assets/img/examples/example-text-ocr-3.jpg',
  'models': 'text-content',
  '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-text-ocr-3.jpg',
  'models' => 'text-content',
  '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-text-ocr-3.jpg',
    'models': 'text-content',
    '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=text-content' \
    -F 'api_user={api_user}' \
    -F 'api_secret={api_secret}'


# this example uses requests
import requests
import json

params = {
  'models': 'text-content',
  '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' => 'text-content',
  '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', 'text-content');
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_22Qd0gUNmRH4GCYLvYtN6",
      "timestamp": 1512483673.1405,
      "operations": 1
  },
  "text": {
      "personal": [
        {
          "type": "phone_number_us",
          "match": "+1 800 222 2408"
        }
      ],
      "link": [],
      "social": [],
      "profanity": [],
      "social": [],
      "extremism": [],
      "medical": [],
      "drug": [],
      "weapon": [],
      "content-trade": [],
      "money-transaction": [],
      "spam": [],
      "violence": [],
      "self-harm": [],
      "ignored_text": false
  },
  "media": {
      "id": "med_22Qdfb5s97w8EDuY7Yfjp",
      "uri": "https://sightengine.com/assets/img/examples/example-text-ocr-3.jpg"
  }
}

Use model (Videos)

Moderate text 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=text-content' \
  -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': 'text-content',
  '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' => 'text-content',
  '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', 'text-content');
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=text-content' \
  -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': 'text-content',
  # 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' => 'text-content',
  // 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', 'text-content');
// 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=text-content' \
    -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('text-content').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(['text-content'])->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(['text-content']).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
      },
      "text": {
        "personal": [],
        "link": [],
        "social": [],
        "profanity": [],
        "social": [],
        "extremism": [],
        "medical": [],
        "drug": [],
        "weapon": [],
        "content-trade": [],
        "money-transaction": [],
        "spam": [],
        "violence": [],
        "self-harm": [],
        "ignored_text": false
      },
     },
     ...
    ]
  },
  "media": {
    "id": "med_gmgHcUOwe41rWmqwPhVNU",
    "uri": "yourfile.mp4"
  },
}

You can use the classes under the text object to analyze text 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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Category	Description
profanity	The profanity category contains following types of terms and expressions: sexual — term or expression that refers to sexual acts, sexual organs, body parts or bodily fluids typically associated with sexual acts discriminatory — discriminatory and derogatory content. Mostly hate speech that instigates violence or hate against groups based on specific characteristics such as religion, national or ethnic origin, sexual orientation or gender identity insult — words or phrases that undermine the dignity or honor of an individual, that are signs of disrespect and are generally used to refer to someone inappropriate — inappropriate language: swear words, slang, familiar/informal or socially inappropriate/unacceptable words or phrases to describe something, or to talk to someone grawlix — string of typographical symbols that are typically used in place of obscenity or profanity read more
personal (pii)	The personal category contains following types of terms and expressions: email — email addresses, including obfuscated ones phone_number_** — phone numbers that are valid numbers in the countries specified through the opt_countries parameter. This includes obfuscated numbers username — usernames ssn — US social security numbers ip — IP addresses, both IPv4 and IPv6 read more
link	URLs to external websites and pages. We can flag domains known to host unsafe or unwanted content read more
extremism	words, expressions or slogan related to extremist ideologies, people or events
weapon	names or terms that related to guns, rifles and firearms
medical	names related to medical drugs
drug	names related to recreational drugs
self-harm	terms related to suicide and self-inflected injuries
violence	expressions of violence such as kicking, punching or harming someone, or threatening to do so
spam	expressions commonly associated with spam or with circumvention, i.e. attempts to send or lure the user to another platform
content-trade	requests or messages encouraging users to send, exchange or sell photos or videos of themselves
money-transaction	requests or messages encouraging users to send money
blacklist (custom)	custom list of terms and expressions read more

Products

MODERATION

REDACTION

REFERENCE

Text Moderation in Images/Videos

Table of contents

Overview

Principles

Use-cases

Categories

Profanity Detection in Images

Personal Information Detection in Images

Email addresses

Phone numbers

Link and URL Detection in Images

Social Account Detection in Images

Custom disallow or allow lists

Languages

Use the model (images)

Moderate text in images

Option 1: Send image URL

Option 2: Send raw image

API response

Use model (Videos)

Moderate text in videos

Option 1: Short video

Option 2: Long video

Option 3: Live-stream

Moderation result

Any other needs?