  Deep learning has emerged as a strong and efficient framework that can be
applied to a broad spectrum of complex learning problems which were difficult
to solve using the traditional machine learning techniques in the past. In the
last few years, deep learning has advanced radically in such a way that it can
surpass human-level performance on a number of tasks. As a consequence, deep
learning is being extensively used in most of the recent day-to-day
applications. However, security of deep learning systems are vulnerable to
crafted adversarial examples, which may be imperceptible to the human eye, but
can lead the model to misclassify the output. In recent times, different types
of adversaries based on their threat model leverage these vulnerabilities to
compromise a deep learning system where adversaries have high incentives.
Hence, it is extremely important to provide robustness to deep learning
algorithms against these adversaries. However, there are only a few strong
countermeasures which can be used in all types of attack scenarios to design a
robust deep learning system. In this paper, we attempt to provide a detailed
discussion on different types of adversarial attacks with various threat models
and also elaborate the efficiency and challenges of recent countermeasures
against them.
