The SAND Lab at University of Chicago has developed Fawkes1, an algorithm and software tool (running locally on your computer) that gives individuals the ability to limit how their own images can be used to track them. At a high level, Fawkes takes your personal images, and makes tiny, pixel-level changes to them that are invisible to the human eye, in a process we call image cloaking. You can then use these "cloaked" photos as you normally would, sharing them on social media, sending them to friends, printing them or displaying them on digital devices, the same way you would any other photo. The difference, however, is that if and when someone tries to use these photos to build a facial recognition model, "cloaked" images will teach the model an highly distorted version of what makes you look like you. The cloak effect is not easily detectable, and will not cause errors in model training. However, when someone tries to identify you using an unaltered image of you (e.g. a photo taken in public), and tries to identify you, they will fail.
Fawkes has been tested extensively and proven effective in a variety of environments, and shows 100% effectiveness against state of the art facial recognition models (Microsoft Azure Face API, Amazon Rekognition, and Face++). We are in the process of adding more material here to explain how and why Fawkes works. For now, please see the link below to our technical paper, which will be presented at the upcoming USENIX Security Symposium, to be held on August 12 to 14.
The Fawkes project is led by two PhD students at SAND Lab, Emily Wenger and Shawn Shan, with important contributions from Jiayun Zhang (SAND Lab visitor and current PhD student at UC San Diego) and Huiying Li, also a SAND Lab PhD student. The faculty advisors are SAND Lab co-directors and Neubauer Professors Ben Zhao and Heather Zheng.