  Despite the outstanding performance of deep neural networks, they are
vulnerable to adversarial attacks. While there are many invisible attacks in
the digital domain, most physical world adversarial attacks are visible. Here
we present an invisible optical adversarial attack that uses a light source to
dazzle a CMOS camera with a rolling shutter. We present the photopic conditions
required to keep the attacking light source completely invisible while
sufficiently jamming the captured image so that a deep neural network applied
to it is deceived.

Cryptography and Security;Machine Learning