NSFW JS

NSFW JS is a popular open-source JavaScript library for detecting Not Safe For Work (NSFW) content directly in the user's browser, created by developer Gant Lab. Its main value lies in enabling decentralized image moderation without sending data to a server, providing instant verification and maximum privacy. The library uses a pre-trained TensorFlow.js convolutional neural network that was trained on an extensive dataset with content classification into five categories: drawings, neutral, sexy, pornography, and hentai.

Key features: the library analyzes images, including files, DataURLs, HTMLImageElement, Canvas, or Tensor, and returns a probability score for each of the five content categories. This allows for automatic blocking or hiding of inappropriate materials in real-time, for example, in user uploads on forums, chats, or social networks. Technically, it works entirely on the client side, integrating into projects via npm or CDN, and does not require complex server infrastructure for analysis. It supports operation in modern browsers and Node.js environments, making it a universal solution for web development.

The uniqueness of NSFW JS lies in its architecture focused on privacy and speed—all processing occurs locally, eliminating the risks of data leaks and delays associated with network requests to APIs. Unlike cloud moderation services, it is free, has no limits on the number of checks, and can work offline. The library is lightweight and focuses exclusively on image classification, offering developers a simple and effective API. It is actively developed on GitHub, where the community contributes to improving the model and recognition accuracy.

Ideal for frontend developers creating platforms with user-generated content, such as social networks, marketplaces, blogs, or educational resources. Excellent for integration into progressive web applications (PWAs) and projects where user privacy and reducing server load are critical. Specific use cases include automatic avatar filtering, pre-moderation of images in comments, and creating safe-for-work digital environments without complex configurations.