Facial Recognition Disruption Technology
AI Blocker uses published adversarial ML research to create wearable facial recognition disruption. This page covers everything you need to know about facial recognition disruption technology — and what you can do about it today.
The Technology
AI Blocker's adversarial strip uses patterns derived from academic research into neural network vulnerabilities. Convolutional neural networks used in facial recognition are sensitive to specific high-frequency perturbations that don't affect human perception but disrupt the feature extraction pipeline.
The strip targets the nose bridge — the highest-weight anchor point in commercial face detection models including those used by Clearview AI, Amazon Rekognition, and Microsoft Azure Face API. Disrupting this anchor point prevents the recognition pipeline from generating a facial embedding, making a match computationally impossible.
Why the Nose Bridge
Facial recognition models normalize detected faces by aligning them to a canonical orientation using 5 landmark points: left eye, right eye, nose tip, left mouth corner, right mouth corner. The nose bridge region carries the highest weight in this normalization step. A pattern that causes unreliable nose bridge detection breaks the entire normalization pipeline.
This is why AI Blocker's strip is positioned at the nose bridge rather than covering the whole face — it's surgical disruption, not camouflage.
Wearability
The AI Blocker strip is designed to look like a medical or cosmetic nose strip at a glance. It's skin-safe, water-resistant, and designed for daily wear. You don't have to look like you're avoiding surveillance to avoid surveillance.
The AI Blocker adversarial strip is available now — designed for daily use, built on published adversarial ML research.
Get AI Blocker — $14.99