Visual Moderation API

A guide to getting started for new & existing customers

Thanks for choosing Hive for your moderation needs - we’re thrilled to have you on board! The purpose of this guide is to show you how to set up a Hive visual moderation project, how to submit classification tasks to the Hive API, and how you might use our model responses to enforce your content policies.

First Steps

To get started with Hive visual moderation, you’ll need to have a Hive project to access our API. Note that projects for each moderation product are separate from each other.

Once you have created a visual project with Hive, log into your project dashboard in order to access your API Key. The API Key is a unique authorization token for your project’s endpoint, and you will need to include the API Key as a header in each request made to the API in order to submit tasks for classification.

Tasks are billed to whichever project they are submitted under, so protect your API Key like you would a password! If you have multiple Hive projects, the API Key for each endpoint will be unique. Be sure you are submitting your content to the correct models.

Submitting Tasks

When submitting a moderation task, you’ll need to provide a public or signed URL for your visual content that is readable by Hive APIs. Alternatively, you can upload local media files by specifying the file path in the request. Supported file types include jpg, png, gif, webp, mp4, and webm. A full list is also available in our documentation here.

You can submit tasks to Hive API endpoints in two ways: synchronous submission and asynchronous submission.

Synchronous Submission

Synchronous endpoints are optimized for a continuous flow of tasks with low latency requirements. If you have real-time moderation needs, you’ll want to submit tasks synchronously - our API returns model outputs directly in the response message.

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NOTE:

For synchronous tasks including video, you should split the video into shorter segments and submit each segment as related tasks in order to minimize response latency. We recommend segments that are no more than 30 seconds long, but can handle segments up to 60 seconds.

Alternatively, you can sample frames (i.e., thumbnails) from your video content and submit those images synchronously.

To automate task submission, you can use the following code template to populate synchronous requests to Hive APIs with links to your visual content (e.g., in response to new posts) and process responses from Hive moderation models.

# Imports
import requests # Used to call most Hive APIs

# Inputs
vis_API_Key = 'your_visAPI_key' # The unique API Key for your visual project (located in your project dashboard).


def handle_hive_response(response_dict):
   # Parse model response JSON and use model results for moderation.
   # See a basic example implementation in the last section.
   pass

def moderate_post_sync(content_url):
   # Example Request (visual, synchronous endpoint, content hosted at URL):
   headers = {'Authorization': f'Token {vis_API_Key}'} # Example for visual moderation tasks
   data = {'url': content_url} # This is also where you would add metadata if desired
   model_response = requests.post('https://api.thehive.ai/api/v2/task/sync', headers=headers, data=data)
   response_dict = model_response.json()
   handle_hive_response(response_dict)

Generally, the API will return a response to the request endpoint within 500ms for a thumbnail image and within 10 seconds for a 30-second video segment.

Asynchronous Submission

Asynchronous submission is preferable for large numbers of concurrent tasks or larger files such as long videos. When submitting tasks asynchronously, you must provide a callback URL (e.g., a webhook) to which the API can send a POST request once results are available. More information and example syntax for asynchronous submissions is available here.

Upon receiving an asynchronous task, the Hive API will send an immediate acknowledgement response with a task identifier and close the connection. When results are ready, the API will send a POST request including model responses to your provided callback URL. You can also call the API manually to trigger a callback for a completed task.

If you need guidance to determine which submission process is best for your use cases, please reach out to [email protected] - we’d be happy to assist you.

For both submission types, the response will be a JSON object that includes a task identifier and other task information, as well as model predictions.

Response

Hive APIs do not remove your content or ban users itself. Rather, the Hive API will return classification metrics from our models that detail the type(s) of sensitive subject matter in the submitted content and our confidence that type of subject matter is there. You can then decide what actions to take based on your sensitivity and content policies.

To see what this might look like for your moderation needs, it may be helpful to walk through an example model response.

Model Response Format & Confidence Scores

Hive’s visual model incorporates a set of submodels, called model heads, each of which are trained to classify different categories of sensitive visual subject matter. Each category is represented as “classes” in the model response object, which includes one or more positive classes and a negative (clean) class. The model response gives predictions from each model head as a confidence score for each class, which correlates with our certainty that visual subject matter is in the class.

To see what this looks like, let’s consider a hypothetical model with two heads: one for identifying sexual (or “NSFW”) content, and another for identifying guns. Suppose the “sexual” model head has two positive classes: general_nsfw (indicating explicit sexual content); general_suggestive (indicating suggestive content without nudity, underwear, etc.); and a negative class general_not_nsfw_not_suggestive (clean).

Similarly, suppose the “gun” model head has three positive classes: gun_in_hand (person holding a gun); animated_gun (e.g., guns appearing in video games); gun_not_in_hand (gun identified, but not being held or used); and a negative class no_gun (clean).

For this hypothetical model, an example of the "output" object in the model response for an image looks like this:

{
  "output": [
    {
      "time": 0,
      "classes": [
        {
          "class": "general_nsfw",
          "score": 0.94
        },
        {
          "class": "general_suggestive",
          "score": 0.05
        },
        {
          "class": "general_not_nsfw_not_suggestive",
          "score": 0.05
        },
        {
          "class": "gun_in_hand",
          "score": 0.88
        },
        {
          "class": "animated_gun",
          "score": 0.04
        },
        {
          "class": "gun_not_in_hand",
          "score": 0.04
        },
        {
          "class": "no_gun",
          "score": 0.04
        }
      ]
    }
  ]
}

Confidence scores for each model head are generated independently, and confidence scores for all positive classes and the negative class sum to 1.

In this example, our hypothetical model is very confident (0.94) that explicit sexual content is in the input image, and reasonably confident (0.88) that at least one person in the input image is holding a gun.

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NOTE:

An actual response from the visual classification model will include many more classes and scores, but will otherwise take the same format - you can see a full list of classes recognized by Hive’s visual models here.


Video and Timestamps

Our API will send a model response object for each image (i.e., task) you submit. For images, including thumbnail images extracted from a video, time is set to 0 by default.

For video tasks, our backend will split the video into representative frames, run the visual model on each frame, and deliver a single model response object that combines the results for each frame. Our default sampling rate for video is 1FPS, but this can be increased if needed. Here, the model response object will contain scores for each class on each sampled frame, and time will be a timestamp for when that frame was captured.

What To Do With Model Results

Once you have decided on a content policy and what actions you want to take, you can design custom moderation logic that implements your policy based on scores pulled from the model response object.

Continuing from our hypothetical model response, here’s a simple example that defines restricted classes and then flags content if scores for one of those classes exceeds a threshold score of 0.9. Let’s say our content policy is fine with animated weapons such as those appearing in video games. Then, we’ll exclude animated_gun from our list of restricted classes so that model responses including an above-threshold confidence score for this class do not trigger moderation actions.

# Inputs 
vis_threshold = 0.9 # Confidence score threshold
banned_classes = ['general_nsfw','general_suggestive', 'gun_in_hand','gun_not_in_hand']
# A selection of classes to moderate from the hypothetical model response shown above.


def handle_hive_response(response_dict):
     scores_dict = {x['class']:x['score'] for x in response_dict['status'][0]['response']['output'][0]['classes']}
    # For a video input, you may want to iterate over timestamps for each frame and take the max score for each class. 
     for class_i in banned_classes:
         if scores_dict[class_i] >= vis_threshold:
             print('We should ban this guy!')
             # Insert your actual moderation actions here
             print(str(class_i) + ': ' + str(scores_dict[class_i]))
             break

Basic example implementation for processing Hive visual model response for an image. Not intended for production use.

Generally, a threshold model confidence score of 0.90 is a good starting point to flag sensitive content in a positive class, but you can adjust based on our model performance for a natural distribution of your data.

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NOTE:

Since some model heads have more than one positive class, an alternative approach could be to compare the negative (clean) class to a low threshold instead. For example, a check to see if no_gun scores below 0.1 is equivalent to checking if scores from all positive “gun” classes sum to greater than 0.9. This approach may be preferable if you are more concerned with recall (i.e., minimizing false negatives) than precision.

When thinking about how to implement your content policy, please see our list of supported classes to see how Hive classifications align with types of content you want to flag. You are free to define different thresholds for different classes, or build in different moderation actions for different classes depending on severity. For example, you may decide to simply tag or age-restrict content that scores highly in the general_suggestive class but delete content that scores highly in the general_nsfw class.

Final Thoughts

We hope that this guide has been a helpful starting point for thinking about how you might use Hive APIs to build automated enforcement of content policies and community guidelines on your platform. If any questions remain, we are happy to help you in designing your moderation solutions. Please feel free to contact us at [email protected] or [email protected].