Entrovix AI

Face Blur

Finds faces in a photo automatically and blurs or pixelates them — with a manual pass so anything the detector misses is covered before you share.

Runs in your browser — nothing is uploaded

Photo and settings

Detection can miss a turned or partly hidden face, so check the preview before sharing — and drag directly on the image to cover anything the detector missed. That manual pass is what makes the result dependable, not the model alone.

Preview — drag to add an area

Add a photo — detected faces blur instantly, and you can drag to cover more.

Why use it

Built to be genuinely useful

Automatic, then manual

Faces are detected and blurred in one step, and you drag boxes over anything the detector missed.

Blur that actually anonymises

Blur strength scales with face size, so a close-up portrait is as unrecognisable as a face in a crowd.

Remove false positives

Every blurred area is listed; one click clears a region the detector got wrong.

Nothing is uploaded

The whole job runs in your browser. Your photo never touches a server, which matters most for the pictures you least want copied.

How it works

Three steps

  1. 1

    Drop a photo in — faces are found and blurred immediately.

  2. 2

    Drag on the image to cover anything missed; click a chip to remove one.

  3. 3

    Download the blurred photo.

Why detection alone is not enough

Face detection is good and imperfect. A face turned away, half-hidden behind a shoulder, in shadow or very small in a crowd can slip past any detector — and a privacy tool that misses one face in a school photo has failed at its entire job.

Fully automatic tools hide this: they blur what they found and say nothing about what they did not. The person sharing the photo has no prompt to check, and the missed face travels with the post.

This tool treats detection as the first pass, not the verdict. It reports how many faces it found, the preview shows exactly what is covered, and dragging a box covers anything else in two seconds. The guarantee comes from you checking a visible result, not from trusting a model.

Blur, pixelate, and doing either properly

A common failure in face blurring is a fixed blur radius: strong enough for a small face in the background, far too weak for a large face in the foreground, which stays recognisable underneath. Here the strength scales with each face's size, so every face gets an effective treatment.

The boxes also grow slightly beyond the detected face. Detectors return a tight crop from eyebrows to chin, and blurring only that leaves the hairline, ears and jaw — which is often enough to identify someone who knows the person.

Pixelation is the alternative style: obviously deliberate, which suits news-style and documentation use, where the viewer should see that something was redacted rather than wonder if the camera slipped. Both are applied at full image resolution.

When blurring is the right call in India

School and society WhatsApp groups are the everyday case: an event photo where your own child can be visible but other children should not be posted without their parents' say. Blurring the other faces takes a minute and removes the entire argument.

Sellers photograph interiors for OLX and rental listings with family photos on the walls; complaint posts about civic issues catch bystanders; accident documentation includes strangers. In each case the subjects never consented to publication, and blurring costs nothing.

One honest caution: blurring the face is not full anonymisation. Clothing, a uniform, a number plate in frame or the location itself can still identify someone. Blur those regions too — the manual box covers anything, not only faces.

FAQ

Questions people ask

Related tools

More image tools

Available tools are linked; the rest are in development.

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