  Deep learning models are known to solve classification and regression
problems by employing a number of epoch and training samples on a large dataset
with optimal accuracy. However, that doesn't mean they are attack-proof or
unexposed to vulnerabilities. Newly deployed systems particularly on a public
environment (i.e public networks) are vulnerable to attacks from various
entities. Moreover, published research on deep learning systems (Goodfellow et
al., 2014) have determined a significant number of attacks points and a wide
array of attack surface that has evidence of exploitation from adversarial
examples. Successful exploit on these systems could lead to critical real world
repercussions. For instance, (1) an adversarial attack on a self-driving car
running a deep reinforcement learning system yields a direct misclassification
on humans causing untoward accidents.(2) a self-driving vehicle misreading a
red light signal may cause the car to crash to another car (3)
misclassification of a pedestrian lane as an intersection lane that could lead
to car crashes. This is just the tip of the iceberg, computer vision deployment
are not entirely focused on self-driving cars but on many other areas as well -
that would have definitive impact on the real-world. These vulnerabilities must
be mitigated at an early stage of development. It is imperative to develop and
implement baseline security standards at a global level prior to real-world
deployment.
