NSFW JS
Identify NSFW images in the browser
Verdict
A quick read before you dig into the full page.
- Best if
- Identify NSFW images in the browser
- Skip if
- Limited user support
- Pick this when
- Client-side Image detection
Overview
NSFWJS is a JavaScript library designed to help identify potentially inappropriate images on a client's browser, without needing to send the image to a server.
The library is powered by TensorFlowJS, which is an open-source machine learning library for JavaScript. The library is trained to recognize particular patterns in the images, with the current accuracy rate at 93%.
The library also incorporates CameraBlur Protection, which is a feature that blurs any images that it identifies as being potentially inappropriate. The library is constantly being improved and updated, with new models being released frequently.
NSFWJS is free to use, and can be modified and distributed under the MIT license. The library also includes a mobile demo, which allows users to test different images on their mobile devices.
Finally, the library is available for download through GitHub, and users are encouraged to report any false positives or contribute to the development of the library.
Best for
Short scenarios where NSFW JS tends to shine.
-
Identify NSFW images in the browser
Example outputs 1
Sample generations and before/after results for NSFW JS.
Access & connections
Where you can use NSFW JS, and what it plugs into.
Platforms
Integrations
Capabilities
What this tool accepts, produces, and is strongest at.
Input
- Image
Output
- Image
Pros & cons
Pros
- Client-side Image detection
- Powered by TensorFlowJS
- 93% Accuracy rate
- Includes CameraBlur Protection
- Constant updates and improvement
- Free to use
- Open-source (MIT License)
- Mobile demo included
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- Available on GitHub
- Community contribution encouraged
- NSFW Patterns recognition
- Content filtering for images
- Allows reporting false positives
- Lightweight (4.2MB)
- In-browser image evaluation
- Doesn't require server interaction
- Modifiable for user needs
- Reduced privacy risks
- Avoids image data transmission
- Consistently learning and adapting
Cons
- Dependent on TensorFlowJS
- No server-side processing
- 93% Accuracy (false positives)
- Potential privacy issues with CameraBlur
- Regular updates required
- Only Javascript implementation
- Limited cross-platform usage
- No real-time detection
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- Reliant on pattern recognition
- Limited user support
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Releases 1
Imported reviews 1
Please forward any material NSFWJS finds directly to me! This kind of thing is not to be trifled with!