papers in adversarial machine learning — adversarial patch

Most uses of adversarial machine learning involve attacking or bypassing a computer vision system that someone else has designed. However, you can use the same tools to generate "unadversarial" examples, that give machine learning models much better performance when deployed in real life.

Self-driving cars rely on vision for safety-critical information like traffic rules, which makes them susceptible to adversarial machine learning attacks. Some carefully placed stickers on a stop sign can make it invisible to autonomous vehicles; or, an adversarial t-shirt can make a person look like a stop sign.

What if breaking into an office was as easy as wearing a special pair of glasses, or putting a sticker on your forehead? It can be, if you make the right adversarial patch. Learn how to use adversarial machine learning to hide from face recognition systems, or convince them that you are someone else.