  The use of deep learning for human identification and object detection is
becoming ever more prevalent in the surveillance industry. These systems have
been trained to identify human body's or faces with a high degree of accuracy.
However, there have been successful attempts to fool these systems with
different techniques called adversarial attacks. This paper presents a final
report for an adversarial attack using visible light on facial recognition
systems. The relevance of this research is to exploit the physical downfalls of
deep neural networks. This demonstration of weakness within these systems are
in hopes that this research will be used in the future to improve the training
models for object recognition. As results were gathered the project objectives
were adjusted to fit the outcomes. Because of this the following paper
initially explores an adversarial attack using infrared light before
readjusting to a visible light attack. A research outline on infrared light and
facial recognition are presented within. A detailed analyzation of the current
findings and possible future recommendations of the project are presented. The
challenges encountered are evaluated and a final solution is delivered. The
projects final outcome exhibits the ability to effectively fool recognition
systems using light.
