Last updated: 17 July 2026
In short: this blurs faces in a video for free, in your browser — the file is never uploaded anywhere. Faces are detected on every frame, and an optional analysis pass groups them into people so you can blur some and keep others sharp. Free covers the first 20 seconds with a small wordmark; Pro does the full clip in HD.
What “free” actually gets you here
You searched for free, so here it is without the runaround. Every feature is free: automatic detection on every frame, the analysis pass that finds who is in the clip, per-person selection, fixed regions, all three masking styles, audio kept in the export. No account, no credits, no queue, no card.
The limits, stated up front: free renders the first 20 seconds of your clip, caps the output at 1280 px wide, and writes a small make-blur.com wordmark into the corner. Pro renders the full duration at up to 1920 px with no wordmark. That is the whole difference — you are paying for length and resolution, not to have a watermark held hostage.
Twenty seconds is short, and there is no point dressing it up. It is short for a reason worth understanding, so let's deal with it head on.
Why our free limit is smaller than everyone else's
Because the computation runs on your machine. There is no farm of GPUs behind this page — the detector loads into your browser tab and your own CPU does the work, frame by frame. Twenty seconds at 12 detections per second is already about 240 neural network passes running in a tab. That is a real constraint, not a pricing lever.
Now flip it around and read the competition's generosity as a spec sheet. When an online blur tool offers you 500 MB and five minutes for free, ask where those five minutes are being processed. Nobody gives away that much compute out of kindness. They can afford it because the work happens on their servers — which means your footage is on their servers. The quota is the architecture, printed on the pricing page.
So the trade is explicit. We are worse on quota and we will not win that argument. We are better on the one thing you cannot undo later: the video does not leave your computer. Pick whichever of those two you actually care about — for a wedding rough cut, probably the quota. For an interview with a source, probably not.
Blur everyone except the people you choose
Here is the part no other browser-based tool does. Most video blur tools answer one question: blur all the faces, yes or no. But that is almost never the actual job. Nobody wants a wedding video where the couple is blurred. Nobody wants an interview where the witness and the reporter get the same treatment.
So the tool does something different. Click Analyse and it seeks through your clip at 12 frames per second, runs the face detector on each sampled frame, and computes a 128-dimension descriptor for every face it finds — a numeric fingerprint of that face. Descriptors within a Euclidean distance of 0.56 of each other are the same person, so they collapse into one cluster. Clusters seen in fewer than two frames get dropped, which is what stops a lamp being catalogued as a wedding guest.
What you see. A row of face thumbnails — one per person the analysis found in your clip, cropped from the frame where that person's face was biggest. Tick who gets blurred. Everyone is ticked by default, so you untick the couple, or the interviewee, or yourself. The render then reuses the stored boxes instead of re-detecting, which is why it plays back at real speed with the audio intact.
And it runs entirely in your browser. Face recognition without a face recognition server is an odd thing to be able to say, but that is what a pre-trained model in a tab gets you. The descriptors are computed, used to group thumbnails, and thrown away when you close the tab. They are never sent anywhere, because there is nowhere to send them.
“Nothing is uploaded” — go ahead and check
Every tool on this search result claims private, local, secure. It has become a checkbox nobody verifies. Verify ours — the video you are about to blur is, by definition, one you would rather not hand to a stranger.
Two tests, under a minute each:
Open DevTools → Network, then use the tool. You will see the page, the fonts, our analytics ping to kipstats.com (page views — no video data) and the face model weights coming down from jsDelivr. You will not see your video going anywhere. There is no request to find, because there is no endpoint that accepts one.
Or load your clip, run one analysis, then kill your wifi. Detect, blur, switch styles, render, download — all of it keeps working with the network off. The model came to you. Your video never went anywhere.
The one nuance, stated rather than buried: the detector weights are fetched from a public CDN the first time you run detection, so the very first pass needs a connection. That request carries no video — it is a plain file download, same as a font. Everything after it is local.
How to blur faces in a video, step by step
- 1Drop your video in.MP4, MOV or WebM — MOV is what your iPhone records, so it goes straight in. It loads off your disk into a video element. There is no upload progress bar because there is no upload.
- 2Decide what gets blurred.Leave it on automatic and every face on every frame gets masked. Or click Analyse: the tool walks the clip, groups the faces into people, and hands you a row of thumbnails. Untick whoever should stay sharp. This step is the reason the page exists.
- 3Drag a box over anything the face detector can't help with.A licence plate, a badge, a whiteboard, a monitor showing a customer record. Fixed regions stay put for the whole clip, so they work when the camera and the object hold still.
- 4Pick the style and the intensity.Black bar if it needs to genuinely hold — it overwrites the pixels. Gaussian or pixelation if it is cosmetic. Details on that difference below, and it matters more than it looks.
- 5Render and download.Frames are drawn to a canvas and recorded in real time with the original audio. You get an MP4 with H.264 video if your browser can mux it, WebM if not. Free stops at 20 seconds; Pro runs the whole clip.
Three things this tool does badly
You will find this out in ten minutes anyway. Better you read it now and decide, than render a clip and feel misled.
No automatic licence plate detection.
The model finds faces. It does not find plates, and we are not going to imply otherwise on a page about privacy. You can drag a fixed rectangle over a plate and it holds for the whole clip — fine for a parked car in a property listing, useless for dashcam footage where the plate drifts across the frame. If dashcam plates are your job, use a tool built for it. That is the honest answer.
No tracker, so the mask can flicker.
We re-detect on every frame rather than tracking a box between keyframes. On a frame where the detector misses — a face turned to profile, heavy motion blur, deep shade — the mask drops for that frame. It is a deliberate trade: a tracker that keeps a stale box where a face used to be will eventually leave a real face uncovered while looking perfectly smooth, and a silent failure is worse than a visible flicker. Keyframe interpolation is on the list.
It is slow on a phone, and there is no batch mode.
Decoding, detecting and re-encoding every frame is real work on real silicon. A laptop handles it; a phone will crawl and get warm. And it is one clip at a time — if you have a folder of forty videos, we are the wrong tool today.
Blur, pixels or black bar — the choice that actually matters
Gaussian blur and pixelation do not delete a face. They average it. Enough signal survives that a neural network can match a blurred face back to the person: 57.75% of the time on faces run through YouTube's own blur, against 2.5% for random guessing, in McPherson, Shokri & Shmatikov (2016). Pixelation scored 57% across 530 people. The networks never had to un-blur anything — they just noticed that a blurred face still correlates with the face underneath.
The caveat, because that number gets abused: this is closed-set identification. The attacker already holds a candidate list and training photos of each person. It is not a button that unmasks a stranger. But think about when someone does hold a candidate list — a school, a forty-person company, a protest with a shortlist. That is the exact situation you were blurring for.
A black bar is not a stronger blur. It is a different operation: the region is filled and the original pixels are overwritten before the frame is ever recorded. There is no residual signal to correlate. So: blur for cosmetics, black bar when someone's safety, job or legal position depends on it.
The GDPR angle, concretely
Two separate things get muddled here, so let's split them.
Is the blurred video still personal data? If you used gaussian blur or pixelation, yes. Recital 26 puts data outside the GDPR only when the person is “no longer identifiable”, judged against “the available technology at the time of the processing and technological developments”. A reversible blur is pseudonymisation — the footage stays personal data, and retention, legal basis and subject access requests all still apply to you. A black bar destroys the pixels and gets you to anonymisation.
Who else touched the footage? This is where local processing pays off in a way a server-side tool structurally cannot match. Upload your clip to an online blur service and that service is a processor acting on your behalf: you need a legal basis, an Article 28 processing agreement, and an answer on where the data sits. Here there is no processor, because no third party ever receives the file. Nothing to sign, no transfer to assess, no vendor to audit — not because we wrote a good privacy policy, but because the architecture never gives us the footage.
Worth being clear about what this is not: it is not legal advice, and it does not settle whether you needed consent to film those people. Blurring afterwards is a mitigation, not a permission slip.
Who this is built for
Not fleet operators or enterprise compliance teams — they have vendors with APIs and we would lose that fight. This is for one person with one clip and a real problem.
The one that made us build the person picker. You want the couple sharp and the two hundred guests who never signed a release blurred. Analyse, untick the couple, render.
In a class of thirty, usually two or three parents refused photo consent. Analyse, tick only those children, and the rest of the class stays normal — which is what everyone actually wants.
The interviewee stays sharp, the people walking behind them get blurred. And the raw footage never touches a third-party server, which is the part that matters when the footage is the story.
You filmed a market. Forty strangers are in it. Blur every face, keep yours — one pass, no rotoscoping.
Blur the staff, keep the incident. Local rendering means the clip never leaves the building, which is usually a hard requirement rather than a preference.
The pipeline, in numbers
Since this page has spent its time telling you to distrust vague privacy claims, here is ours with the numbers attached.
| Detector | face-api.js SSD MobileNetV1, minConfidence 0.3 |
|---|---|
| Identity | 128-d face descriptor per detection, clustered on Euclidean distance < 0.56 |
| Analysis sampling | 12 frames per second across the clip |
| Person threshold | A cluster needs ≥ 2 detections to count as a person (kills one-frame noise) |
| Live mode | Full frame + a 2-tile horizontal pass on frames ≥ 1280 px wide |
| Region padding | +12% / +18% live · +14% / +20% from analysis, ellipse |
| Styles | Gaussian blur, pixelation, black bar |
| Input | MP4, MOV (QuickTime), WebM |
| Output | MP4 / H.264 (avc1) when your browser can mux it, WebM otherwise. Audio kept. |
| Free | First 20 seconds · output capped at 1280 px wide · small wordmark |
| Pro | Full duration · up to 1920 px · no wordmark |
What Pro costs, and what it is actually for
The 30-day pack is 9,90 € once — not a subscription, it just stops. It exists because most people blurring a video are doing it for one wedding, one school year, one story. Pay, finish the job, leave.
What it changes: your full clip instead of the first 20 seconds, output up to 1920 px instead of 1280, and no wordmark. The first two are the point. A blur tool that only ever gives you 20 seconds is a demo — Pro is what turns it into something you can deliver. The watermark going away is the least interesting part of it, which is why it is listed last.
FAQ
Is my video uploaded to a server?+
No. The file is read straight off your disk into a <video> element and a canvas, and every frame is detected, blurred and re-encoded in your browser. There is no endpoint on our side that accepts a video, so there is nothing to upload it to. You can confirm it in about ten seconds: load a clip, then turn off your wifi. Everything keeps working.
Does the blur follow a face that moves around?+
Yes, because we re-detect on every frame rather than tracking. The detector runs on each frame and the blur lands where the face is at that instant, so it follows a person who walks, turns or leaves and re-enters the shot. The trade-off is honest: there is no tracker filling in the gaps, so on a frame where the detector misses a face — heavy motion blur, a profile turned right away, a face in deep shade — the mask can flicker for that frame. We chose per-frame detection because a stale box that lingers where a face used to be is worse than a flicker.
Can I blur everyone except one person?+
Yes, and this is the thing worth coming here for. Click Analyse and the tool walks the clip at 12 frames per second, computes a 128-dimension descriptor for every face and groups them into people. You get a row of thumbnails — one per person it found — and you tick who gets blurred. Blur the forty wedding guests, keep the couple sharp. It works across the whole clip, not frame by frame.
What video formats can I use?+
MP4, MOV (the QuickTime container your iPhone records) and WebM. The export comes out as MP4 with H.264 video when your browser can mux it — Chrome 130 and up can — and falls back to WebM when it cannot. Audio from the original is carried into the export in both cases.
How long can the video be?+
Free covers the first 20 seconds of whatever you drop in, and caps the output at 1280 px wide with a small make-blur.com wordmark. Pro renders the full duration at up to 1920 px with no wordmark. There is no queue and no per-file credit either way, because there is no server metering you.
Why is the free limit 20 seconds when other tools allow hundreds of megabytes?+
Because the maths runs on your laptop, not on ours. A tool that happily accepts a 500 MB upload is telling you something specific about where your video is going: onto their infrastructure. Our limit is a real constraint of doing the work locally, not a lever to make you pay. It also means the 20 seconds you get free are unlimited in number — blur as many clips as you like, no credits, no signup.
Does it work on mobile?+
It runs in a mobile browser, but be realistic about it: decoding, detecting and re-encoding every frame is heavy work, and a phone will be slow and will get warm. For anything beyond a short clip, use a laptop. We would rather tell you that now than have you watch a progress bar crawl on a phone.
Is the blur permanent, or can it be undone?+
It depends which style you pick, and this catches people out. The exported video is re-encoded from the masked frames, so nothing hidden is smuggled inside the file. But gaussian blur and pixelation average pixels rather than delete them, and enough signal survives that a neural network can match a blurred face back to a person — 57.75% of the time in the McPherson, Shokri and Shmatikov study (2016), against 2.5% for guessing. The black bar overwrites the pixels. Nothing survives it. If someone's safety depends on the mask, use the black bar.
Does blurring faces make my video GDPR-compliant?+
Not on its own. Recital 26 puts data outside the GDPR only when the person is no longer identifiable, judged against technology available now. A reversible blur is pseudonymisation, so the footage stays personal data. A black bar destroys the pixels and gets you to anonymisation. Separately, processing locally means no third party ever holds the footage — there is no processor to sign a DPA with, and no transfer to assess.
Can you blur licence plates automatically?+
No, and we would rather say so than let you find out on your dashcam footage. There is no plate detector in the engine — the model we ship finds faces. You can still mask a plate: pause on the frame, drag a rectangle over it, and it stays masked for the whole clip. That works when the camera and the plate hold still. On moving dashcam footage where the plate drifts across the frame, a fixed rectangle is not good enough, and we are not going to pretend otherwise. Use a plate-specific tool for that.
Does it work offline, or on a locked-down corporate network?+
Yes, once the page and the face model have loaded. The model weights come from a public CDN (jsDelivr) the first time you hit Analyse or blur faces — a plain file download carrying no video. After that, cut the connection and the tool keeps detecting, blurring and exporting. That is why this works on a plane, and why it works in places where uploading the footage to an online tool would be the end of the conversation.
Blur a video now — free, in this tab
First 20 seconds free, unlimited clips, no account. Blur everyone, or analyse and pick who stays sharp. Either way it never leaves your computer.
Also here: the photo face blur · and masking a licence plate.
Sources
- R. McPherson, R. Shokri, V. Shmatikov, “Defeating Image Obfuscation with Deep Learning”, arXiv:1609.00408, September 2016.
- Regulation (EU) 2016/679 (GDPR), Recital 26 (anonymous data) and Article 28 (processors).
- Face detection and descriptors: @vladmandic/face-api (SSD MobileNetV1), running in your browser.
Page written and maintained by the Make Blur team (KIPDEV). Last reviewed . The detection, clustering and export figures describe the engine currently in production and are updated when it changes.