QR Code Moderation in Images/Videos
qr-contentThe QR Code Moderation API is useful to detect if an image or video contains QR codes and determine if the content of those QR codes is problematic.
Overview
The QR Code Moderation API is useful to detect if an image or video contains QR codes and determine if the content of those QR codes is problematic.
Problematic QR codes are ones that contain profanity, or personal information, or links to bad websites and URLs such as adult content, malware, illegal or otherwise unwanted content. The API will tell you what type of content has been found in QR codes, if any.
Just like our other Image Moderation APIs, this API performs the analysis entirely automatically. There are no humans reviewing your content. This helps us achieve very fast turnaround times — less than a second — and very high scalablity.
Principles
The QR Code Moderation API is a robust way to sanitize images or videos that might contain QR codes. It works for images with multiple QR codes, and works out-of-the-box with QR codes of various sizes and under different angles.
The QR Code Moderation API for Images works in several steps:
- Detection of QR codes contained in the image
- Decoding of the QR code content
- Analysis of the content through our moderation engine
Use-cases
- Protect users from nefarious links embedded in QR codes
- Restrict bypass techniques attempting to lure your users away to other websites/apps through QR codes
- Flag users posting profanity, obscene or illegal content through QR codes
Categories
The rules are grouped into categories, to help you implement custom filters based on the type of flagged content.
| Category | Description |
| link | URLs to external websites and pages. We can flag domains known to host unsafe or unwanted content read more |
| personal (pii) | The personal category contains following types of terms and expressions:
|
| profanity | The profanity category contains following types of terms and expressions:
|
| spam | expressions commonly associated with spam or with circumvention, i.e. attempts to send or lure the user to another platform |
Link and URL Moderation in QR codes
Links and URLs will be detected and flagged as such in QR codes. They will be returned under the link key in the API response. Here is an example:
{
"qr": {
"link": [
{
"type": "url",
"match": "http://harmfulsiteexample.com",
"category": "unsafe"
}
]
}
}
In addition to detecting URLs in QR codes, you can also moderate the link to determine if the link is unsafe, contains adult or unwanted content or is deceptive, through the category field. More details are available on the URL and link moderation page
Profanity Detection in QR codes
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.
{
"qr": {
"profanity": [
{
"type": "insult",
"match": "idiot",
"intensity": "medium"
}
]
}
}
Personal Information Detection in QR codes
Email addresses
Email addresses will be detected and flagged as such in the image.
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
Languages and Recommendations
Languages
English is the default language used for the 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 |
| Korean | ko |
| Norwegian | no |
| Polish | pl |
| Portuguese | pt |
| Russian | ru |
| 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 QR codes in images
Let's say you want to moderate the following image:
You can either share a URL to the image, or upload the image file.
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=qr-content' \
-d 'api_user={api_user}&api_secret={api_secret}' \
--data-urlencode 'url=https://sightengine.com/assets/img/examples/example-qr-600.jpg'
# this example uses requests
import requests
import json
params = {
'url': 'https://sightengine.com/assets/img/examples/example-qr-600.jpg',
'models': 'qr-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-qr-600.jpg',
'models' => 'qr-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-qr-600.jpg',
'models': 'qr-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 |
| 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 image file
Here's how to proceed if you choose to upload the image file:
curl -X POST 'https://api.sightengine.com/1.0/check.json' \
-F 'media=@/path/to/image.jpg' \
-F 'models=qr-content' \
-F 'api_user={api_user}' \
-F 'api_secret={api_secret}'
# this example uses requests
import requests
import json
params = {
'models': 'qr-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' => 'qr-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', 'qr-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 | file | 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
},
"qr": {
"personal": [],
"link": [
{
"type": "url",
"match": "http://harmfulsiteexample.com"
}
],
"social": [],
"profanity": [],
"spam": []
},
"media": {
"id": "med_22Qdfb5s97w8EDuY7Yfjp",
"uri": "https://sightengine.com/assets/img/examples/example-qr-600.jpg"
}
}
Successful Response
Status code: 200, Content-Type: application/json| Field | Type | Description |
| status | string | status of the request, either "success" or "failure" |
| request | object | information about the processed request |
| request.id | string | unique identifier of the request |
| request.timestamp | float | timestamp of the request in Unix time |
| request.operations | integer | number of operations consumed by the request |
| qr | object | results for the model |
| media | object | information about the media analyzed |
| media.id | string | unique identifier of the media |
| media.uri | string | URI of the media analyzed: either the URL or the filename |
Error
Status codes: 4xx and 5xx. See how error responses are structured.Use the model (videos)
Moderate QR codes 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=qr-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': 'qr-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' => 'qr-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', 'qr-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 | file | 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=qr-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': 'qr-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' => 'qr-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', 'qr-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 | file | 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=qr-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('qr-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(['qr-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(['qr-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
},
"qr": {
"personal": [],
"link": [],
"social": [],
"profanity": [],
"spam": []
},
},
...
]
},
"media": {
"id": "med_gmgHcUOwe41rWmqwPhVNU",
"uri": "yourfile.mp4"
},
}
You can use the classes under the qr object to detect and analyze QR codes in the video.
Any other needs?
See our full list of Text models for details on other filters and checks you can run on your text content. You might also want to check our Image & Video models to moderate images and videos. This includes moderation of text in images/videos.