Adversarial Pattern Prevents Surveillance Cameras from Detecting You

A breakthrough in surveillance evasion technology has been developed, allowing individuals to avoid detection by surveillance cameras and license plate readers.

Bill Swearingen has spent the past year running tests to produce a computer-generated pattern that can block surveillance cameras from detecting people or objects. After 31 million tests, he has successfully created patterns that can prevent some of the most commonly deployed license plate readers and surveillance cameras from detecting what the pattern covers.

His project, noRecognition, allows people to escape the automatic detection and algorithmic surveillance used across the U.S. and beyond. The patterns do not block surveillance cameras from recording video footage, but instead scramble the camera's ability to identify objects, people, or faces, so that the cameras do not trigger any detection alerts.

"Privacy is a fundamental right," Swearingen told TechCrunch. He described his patterns as a way to allow people to "opt-out of being tracked."

The patterns were successfully demonstrated at the Def Con cybersecurity conference in Las Vegas, where a vehicle covered with one of Swearingen's newest patterns was able to evade detection by a Flock camera.

Swearingen's model creates new patterns every minute, each batch mathematically better than the last. The next step is getting the patterns into the hands of people who want them, he said.

The noRecognition project also has a crowdsourcing campaign to help fund the sale of early merchandise featuring the patterns, from T-shirts to hoodies. Swearingen said the aim is for the patterns to be high quality and resolution good enough to work from a distance, while also looking aesthetically fashionable.

• The project builds on earlier work that showed it was possible to block camera detections by using computer-generated patterns.
• Swearingen's model uses a reinforcement learning approach to train itself on which patterns work and which do not against specific camera algorithms.
• The model has already defeated 11 open-source detection algorithms, including those used by Flock license plate readers and Axon body-worn cameras.
• Swearingen is keeping his strongest patterns off the internet to prevent camera makers from defeating them.

This breakthrough in surveillance evasion technology raises questions about the balance between security and privacy. As surveillance cameras become more ubiquitous, individuals may need to find new ways to protect their right to privacy.

The noRecognition project is a step in that direction, providing individuals with the tools they need to evade detection and protect their privacy. However, the project also highlights the need for a more nuanced discussion about the use of surveillance technology and the impact it has on individuals and society.

The future of surveillance technology is likely to be shaped by the tension between security and privacy. As individuals and governments grapple with this tension, the noRecognition project offers a glimpse into a possible future where individuals have more control over their own privacy and security.