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Free Photo Anonymizer (Face Blur)
Auto-detect and blur faces to anonymize photos, fully private|4.8 (531)
A free, private photo anonymizer that automatically finds faces in a picture and blurs or pixelates them so identities are protected before you share the image. It is built on Google MediaPipe and its BlazeFace short-range model, an open-source face detector that runs directly in your browser through WebAssembly and WebGL. Upload a JPG, PNG, or WebP and the tool draws it to a canvas, detects every face on your own device, and redacts each one with an adjustable blur radius or pixel size. Because detection and redaction both happen locally, the image is never uploaded, logged, or stored, which makes it safe for personal photos, screenshots, client work, and street photography. You can switch between blur and pixelate, tune the strength with a slider without re-running detection, see how many faces were found, and download the anonymized result as a PNG. The MediaPipe model downloads once on first use and is cached by your browser afterward.
Free and provided as is, without warranty. Use at your own risk. Terms
Blur faces to anonymize photos without uploading anything
Most online face blur tools ask you to upload your photo to a server, run it through a hosted vision model, and send the redacted image back, which means the very picture you are trying to keep private leaves your control. This free photo anonymizer works the other way around: the face detector runs inside your browser tab. Once the model is downloaded, your image is decoded, detected, and redacted on your own device, so the file never touches a server and there is no limit on how many photos you can process.
That makes it a strong fit for personal photos, screenshots you want to share in a bug report, client work, and street or travel photography where strangers ended up in frame. You get a full-resolution PNG with every detected face blurred or pixelated, and you can confirm nothing is uploaded by watching the Network tab in your browser DevTools while it works. It is anonymization, not just detection: the faces are actually hidden before you download the result.
Powered by Google MediaPipe and BlazeFace, running in your browser
This tool is built on Google MediaPipe, the open-source framework for on-device machine learning, and its BlazeFace short-range model, a fast, lightweight face detector designed to run in real time on everyday hardware. MediaPipe loads the model through WebAssembly and accelerates it with WebGL, so face detection happens entirely on your device with no backend involved.
When you upload an image, the tool draws it to a canvas, asks MediaPipe for the bounding box of every face, and then redacts each region with either a Gaussian blur or a mosaic pixelation. Because the detected positions are stored, the strength slider re-applies the effect instantly without running detection again, so you can fine-tune the level of anonymization and preview it live. The model files download once from Google, are cached by your browser, and work offline afterward. Both MediaPipe and BlazeFace are released under the permissive Apache 2.0 license.
Blur, pixelate, and privacy-friendly sharing under GDPR
Choose blur when you want a soft, natural mask over each face, and pixelate when you want an obvious, deliberate redaction of the kind used in news and documentary images. Both remove one of the most identifying pieces of personal data in a photo, which is exactly what privacy-by-design and GDPR expectations call for before you publish images of people who did not consent to being shown. Turning up the strength slider makes the redaction stronger and harder to reverse.
Treat this as a fast complement to manual redaction rather than a replacement for judgment. Review the result to make sure every face was caught, since heavily turned, very small, or shadowed faces can be missed, and check for other identifying details such as name tags, license plates, and reflections that a face detector will not touch. Used this way, the tool turns careful, privacy-friendly sharing into a few seconds of work instead of a session in a heavy photo editor, and it does it without ever sending your image anywhere.
Who relies on face blurring, and why it matters to them
Journalists and documentary photographers blur bystanders and sources whose identity needs protecting before an image runs publicly, especially in contexts where being identified could carry real risk. Researchers publishing photos from a user study or a public-space observation blur every face that is not the specific consenting subject, since institutional review boards typically require exactly this kind of redaction before images can be shared or published.
Event photographers and community organizers scrub crowd shots before posting them publicly, since a photo taken at a public event can still capture someone who would rather not be identifiable online, and parents and caregivers blur other people's children who appear incidentally in a photo of their own kid before sharing it in a group chat or on social media.
How this compares to Photoshop and dedicated anonymization software
Manually blurring faces in Photoshop or GIMP gives full control over exactly what gets redacted, but it means selecting each face by hand, tool that costs a Creative Cloud subscription (roughly 23 dollars a month) if you go the Adobe route, and doing it for a photo with a dozen faces in a crowd is genuinely tedious. Dedicated anonymization software used in research and video production, tools like Deface or commercial video-redaction platforms, automate detection similarly to this tool but are typically built for batch video processing and priced for institutional or enterprise use.
This tool sits in between: automatic detection means you are not manually drawing a blur region over every face, but it runs entirely for free in a browser tab with no software installation or per-project licensing. For a single photo or a handful of images, that combination of automation and zero cost covers what most people actually need; for redacting hours of video footage at an institutional scale, a dedicated pipeline tool is still the better fit.
What automated face detection cannot guarantee
Face detection models, BlazeFace included, are trained on datasets that do not represent every face angle, lighting condition, and skin tone equally well, and published research on face detection systems has repeatedly found accuracy gaps across these factors. In practice this means a detector can be more likely to miss a face that is partially turned, poorly lit, or simply underrepresented in its training data, which is exactly why the tool shows a face count and why reviewing the result before sharing matters more than trusting the automation blindly.
It is also worth being clear that blurring a face reduces identifiability but is not an absolute, unbreakable anonymization guarantee against a sufficiently motivated attacker with other context (clothing, location, companions in the photo), and automated blurring is never a substitute for actually asking someone whether they are comfortable being in a photo you plan to share. Use this tool as a strong, fast privacy safeguard, not as a replacement for consent or for a careful manual check on anything genuinely sensitive.
How It Works
Upload an image (JPG, PNG, or WebP) and the tool draws it to a canvas, then loads the MediaPipe FaceDetector once and runs it on your own device: nothing is sent to a server.
Every detected face is redacted in place using either a Gaussian blur or a mosaic pixelation, and the tool shows you how many faces it found.
Adjust the blur radius or pixel size with the strength slider, switch between blur and pixelate, then download the anonymized image as a PNG.
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Key Features
Privacy & Trust
Use Cases
Limitations
- The first run downloads the MediaPipe FaceDetector model (a few megabytes), which is cached by your browser afterward
- BlazeFace is tuned for reasonably front-facing faces, so heavily turned, very small, occluded, or low-light faces may be missed: always review the result before sharing
- It detects faces, not other identifying details such as name tags, tattoos, license plates, or reflections, which you should redact manually if needed
- Blur and pixelation reduce identifiability but are not a guarantee of anonymity for a determined attacker, so treat strong strength settings and additional redaction as good practice for sensitive images
- Very large images are processed on your CPU and GPU, so extremely high-resolution photos may take a moment to detect and redraw
Frequently Asked Questions
Is this photo anonymizer really free?
Yes, it is completely free with no signup, no account, and no usage limits. Because the face detector runs on your own device through Google MediaPipe instead of a paid cloud service, there are no per-image costs to pass on. You can anonymize as many photos as you want, as often as you want, without a credit card, an API key, or a rate limit.
Is my image uploaded to a server?
No. The entire process, detecting faces and blurring them, happens locally in your browser using MediaPipe, WebAssembly, and WebGL. After the model is downloaded once, your photo is processed on your own device with zero network requests carrying it. Nothing is uploaded, logged, or stored, which makes this safe for personal photos, screenshots, and confidential work. You can confirm this by opening the Network tab in your browser DevTools while you anonymize an image.
How is this different from a tool that just detects faces?
A detector only draws boxes around faces to show you where they are, which does nothing to protect anyone. This tool is a privacy redaction tool: it detects each face and then actually blurs or pixelates that region of the image so the person can no longer be identified, and it lets you download the anonymized result. The goal is not to display detections but to hide faces before you share the photo.
What is the difference between blur and pixelate?
Blur applies a smooth Gaussian softening over each face so features fade away gradually, which tends to look more natural in a photo. Pixelate replaces each face with a coarse mosaic of large blocks, which reads as a deliberate, obvious redaction and is common in news and documentary images. Both hide identity: choose blur when you want a subtle result and pixelate when you want it clear that the face was intentionally masked. You can switch between them at any time.
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