How Blurify Works — and Who It's For

When publishing photos from protests, conflict zones, or undercover investigations, journalists face a critical obligation: protect the people in their images. A visible face, a distinctive tattoo, or a unique piece of clothing can be enough to identify someone and put them at risk.

Blurify lets reporters blur identifying features directly in the browser — no cloud upload, no third-party server ever sees the image. This matters because even "private" cloud storage operated by a vendor is subject to legal subpoenas and data breaches. With Blurify, the original unblurred file never leaves the journalist's own machine.

Legal and HR workflows regularly involve documents that must be partially redacted before they can be shared: personnel files with salary information removed, contracts with proprietary clauses blacked out, or settlement agreements with identifying details stripped.

Traditional redaction tools are expensive, require installation, or force you to upload sensitive documents to a cloud service. Blurify handles PDFs entirely in the browser — draw solid redaction blocks over the sections that need to be hidden, then export a clean copy. Nothing is transmitted or stored externally at any point in the process.

Street photographers, travel vloggers, and social media creators frequently need to blur bystanders' faces before posting. Many platforms require it; many creators simply want to respect the privacy of people who didn't consent to appear in their content.

For images, Blurify's ellipse tool traces naturally around a face in seconds. For videos, keyframes let you track a face across several seconds of footage without manually adjusting the region frame-by-frame. Export directly as MP4 or WebM, ready to upload.

Sharing a screenshot to report a bug or write documentation almost always means accidentally leaking something sensitive — an API key in a terminal output, a customer email in a support queue screenshot, an internal hostname in a network diagram.

Blurify's rectangle tool makes it trivial to black out those details before the screenshot goes into a Jira ticket, a public GitHub issue, or a blog post. Because it runs in the browser, it fits naturally into any OS without installing additional software or granting another tool access to your screen.

HIPAA and equivalent regulations in other jurisdictions require that patient-identifiable information be removed from images before they can be used in case studies, medical publications, conference presentations, or training datasets.

Blurify allows clinicians and researchers to anonymize faces, remove visible name tags, and obscure other identifying features from clinical photos — all without uploading patient imagery to an external service. The processed image is exported locally, keeping the original unmodified file on the practitioner's own device.

Security footage, dashcam recordings, and witness videos often need to be shared with law enforcement, insurers, or courts — but may contain bystanders who are not relevant to the incident and whose privacy should be protected.

Blurify's video workflow renders each frame with blur regions applied using ffmpeg compiled to WebAssembly, then re-encodes the result entirely in the browser. The output video can be exported as MP4 or WebM without the original footage ever leaving the user's device.

When you load an image or PDF into Blurify, the file is read into memory as an ArrayBuffer using the browser's FileReader API. For images, it's decoded into an HTMLImageElement and drawn onto an offscreen HTML5 Canvas. For PDFs, pdf.js renders each page into a canvas element. At no point is the file data sent to a network request.

Blur effects are applied using the Canvas 2D context's filter property — a Gaussian blur algorithm executed entirely by the browser's rendering engine. On browsers that don't support the filter property (Safari prior to version 18), Blurify falls back to a two-pass downsample and upsample technique using imageSmoothingQuality, which approximates the same blur effect.

Video re-encoding is handled by ffmpeg compiled to WebAssembly (ffmpeg.wasm). This is the full ffmpeg binary, running inside your browser's JavaScript engine — no server involved. The video is played back in a hidden HTML video element, each frame is captured onto a canvas with blur regions applied, and the resulting canvas stream is recorded using the MediaRecorder API.

For MP4 output on browsers that don't support native MP4 recording (Chrome and Firefox), Blurify records to WebM first, then uses ffmpeg.wasm to transcode to MP4 locally. The entire transcode happens in a Web Worker, keeping the UI responsive during the operation.

Blurify collects basic, anonymous page-view analytics via Google Analytics and Vercel Analytics — the same aggregate data collected by most websites (page views, country, browser type, session duration). Google AdSense may set cookies for ad personalisation, subject to your cookie consent choice.

Blurify does not collect, transmit, or store any information about the files you process. Your images, videos, and PDFs are handled entirely within your browser's memory and are never sent to any server. The original files remain unchanged on your device; only the exported output is written to disk when you click download.