For Privacy & Compliance

Anonymize images that blur can't protect

Blurring faces and plates feels safe but isn't — ML deblurring can reverse it. Peelaway replaces identifiable data with natural-looking content that contains zero recoverable information. Built for the scale that compliance demands.

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Beyond blur

Every type of identifiable information in images — detected, removed, and replaced with content that can't be reversed.

Face anonymization

Detect and remove identifiable faces from images — street photography, security footage stills, crowd shots. The AI reconstructs the scene naturally without leaving obvious blurred patches.

License plate removal

Vehicle plates in street-level imagery, dashcam stills, and property photos. Removed and replaced with natural-looking surfaces — not crude pixelation.

Personal information redaction

Visible addresses, phone numbers, email addresses, and account numbers in document photos and screenshots. Removed completely from the image data.

Screen content sanitization

Monitors and screens visible in photos showing private data, login sessions, or internal dashboards. The displayed content is replaced naturally.

Name & badge removal

Name badges, ID cards, and visible identification in event, workplace, and security photos. Removed while preserving the person and clothing.

Batch anonymization via API

Process thousands of images programmatically. The API scales horizontally — submit images in parallel, poll for completion, download results. Costs nothing when idle.

Blur is a liability. Removal is compliance.

Pixelation and Gaussian blur were the standard for decades. Then ML deblurring showed they're reversible. A blurred face is a hidden face, not an anonymized face — and regulators increasingly understand the difference.

Peelaway uses instruction-based image editing to replace identifiable content with entirely new, context-appropriate pixel data. The original face, plate, or text doesn't exist in the output file. That's not masking — that's actual anonymization.

True removal, not masking

Blur and pixelation can be reversed with ML deblurring. Peelaway replaces the original pixels entirely — the data doesn't exist in the output file.

Natural-looking output

No crude black rectangles or obvious pixelation that says 'something was redacted here.' The edited areas blend seamlessly with the scene.

Scales to compliance volume

GDPR, CCPA, and privacy regulations affect millions of images. The serverless API handles 10 images or 10,000 — same integration, automatic scaling.

No data retention

Images are processed and results are available for download. No permanent storage of input images or personal data on Peelaway's infrastructure.

Frequently asked questions

Is blurring faces actually reversible?
Yes. Research has shown that pixelation and Gaussian blur can be partially reversed using ML-based deblurring and super-resolution techniques. The risk depends on the blur strength, but it's a real concern for compliance. Peelaway generates entirely new pixel data — the original face isn't in the output at all.
Can this help with GDPR compliance for image datasets?
Yes. GDPR requires that identifiable personal data be anonymized when consent isn't available. Peelaway can anonymize faces, license plates, and visible personal information at scale via the API. The output contains no recoverable personal data.
How does it detect what needs to be anonymized?
Peelaway uses open-vocabulary AI detection (Grounding DINO). You describe what to find — 'faces', 'license plates', 'name badges' — and the AI locates all instances in the image. This is more flexible than fixed-category detectors.
What about street-level imagery for mapping or real estate?
Exactly the use case. Google blurs faces and plates in Street View for a reason. Real estate virtual tours, mapping services, and street-level photography all need anonymization. Peelaway handles this at scale — and the result looks cleaner than standard blur.
Can we process dashcam or security camera stills?
Yes. Dashcam stills, security camera frames, and surveillance imagery can be anonymized. Faces, plates, and identifying features are removed. The scene context (road, building, environment) is preserved.

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