NSFW JS: Client-Side Content Moderation Library
NSFW JS helps software developers implement basic image moderation without needing a dedicated backend server. It is designed for teams that need a client-side layer to flag potentially inappropriate content.
At a glance
- Category
- Software Development
- Best for
- Software developers, Software companies, Web application owners, Frontend engineers
- Pricing
- Pricing was not clearly available from the provided evidence. Buyers should confirm current pricing on the vendor website.
- Key use cases
- In-browser image screening, Initial content flagging, Privacy-focused moderation
- Official website
- nsfwjs.com/
NSFW JS is a JavaScript library designed for indecent content checking. Unlike moderation tools that require uploading images to a cloud server, this library performs its analysis on the client side within the browser. This approach supports digital workflows and may reduce server load for web applications.
It is intended for software development teams and companies building web applications that allow user-uploaded images. By using machine learning models like MobileNetV2Mid, the library can help flag images that may contain unseemly content before they are displayed.
Buyers should be aware that the tool is designed to err on the side of caution, meaning it may flag clean images as inappropriate. Developers can choose from different model sizes and accuracy levels to fit their application's performance requirements.
The project is open-source, allowing technical teams to review the algorithm and contribute to the data scraper to help refine future models.
Key Features
Executes image analysis directly in the browser, which may reduce the need to send images to a remote server.
Uses models such as MobileNetV2Mid to identify indecent content.
Supports different model versions, including a 2.6MB version with 90% accuracy and a 4.2MB version with 93% accuracy.
The underlying code is public and hosted on GitHub for transparency and community contribution.
Includes functionality for blur protection.
Use Cases
Identifying potentially indecent images in a web application before they are uploaded or displayed.
Using a client-side filter to flag content that may require further review by a human moderator.
Checking images for inappropriate content without transferring user data to a third-party server.
Best For
Pricing
Pricing was not clearly available from the provided evidence. Buyers should confirm current pricing on the vendor website.
FAQ
It performs analysis within the client's browser using machine learning models, so images are not sent to a server for checking.
Depending on the model chosen, accuracy ranges from 90% (2.6MB model) to 93% (4.2MB and larger models).
Yes, the tool is designed to error on the side of flagging content as inappropriate rather than missing indecent images.
Source category: Software Development
Source subcategory: Computer Vision
