Analysis updated 2026-05-18
Automatically blur or pixelate faces in a batch of photos before publishing them.
Anonymize sensitive regions in a video frame by frame using local browser processing.
Manually paint or draw over areas automatic detection missed.
Run a local Python and OpenCV backend for faster face detection without sending data online.
| web3privacy/w3pn-anonymizer | 0xkinno/neuralvault | 0xmayurrr/ai-contractauditor | |
|---|---|---|---|
| Stars | 1 | 1 | 1 |
| Language | TypeScript | TypeScript | TypeScript |
| Setup difficulty | easy | hard | easy |
| Complexity | 2/5 | 4/5 | 2/5 |
| Audience | general | developer | developer |
Figures from each repo's GitHub metadata at analysis time.
Works directly in the browser, the optional Python OpenCV backend needs a separate local setup.
W3PN Anonymizer is a free, open-source tool for blurring or hiding faces and sensitive regions in photos and videos, built with privacy as the core principle. Everything runs inside your web browser by default, your images and videos never leave your device, and there are no analytics, cookies, or tracking of any kind. When you load a photo or video, the tool uses a face detection model called YuNet to automatically find faces. You can then apply any of over 14 effects to those detected regions, including blur, pixelate, blackout, emoji overlay, glitch, thermal, and halftone. If automatic detection misses something, you can also draw rectangles or paint with a brush over any area you want to anonymize. For images, you can additionally adjust color settings like brightness, contrast, and saturation before exporting in six formats including JPEG, PNG, and WebP. Video support works frame by frame. The tool processes, masks, and encodes video locally using the browser's built-in Canvas API and MediaRecorder. It handles common video formats such as MP4, WebM, MOV, and AVI. You can even fix individual frames manually by pulling them out as images, editing them, and baking the changes back into the next export. For heavy workloads, a batch mode lets you process and anonymize hundreds of photos at once and download the results as a ZIP file. An optional Python backend using OpenCV can handle face detection on your own machine's localhost, and it never sends data to the internet. The tool is written in TypeScript and can also be built as a desktop application using Electron.
A privacy-first, browser-based tool that automatically detects and blurs faces in photos and videos, with all processing done locally on your device.
Mainly TypeScript. The stack also includes TypeScript, Electron, Python.
License is not stated in the available README content.
Setup difficulty is rated easy, with roughly 5min to a first successful run.
Mainly general.
This repo across BitVibe Labs
Verify against the repo before relying on details.