  Over the past decade, Deep Learning has emerged as a useful and efficient
tool to solve a wide variety of complex learning problems ranging from image
classification to human pose estimation, which is challenging to solve using
statistical machine learning algorithms. However, despite their superior
performance, deep neural networks are susceptible to adversarial perturbations,
which can cause the network's prediction to change without making perceptible
changes to the input image, thus creating severe security issues at the time of
deployment of such systems. Recent works have shown the existence of Universal
Adversarial Perturbations, which, when added to any image in a dataset,
misclassifies it when passed through a target model. Such perturbations are
more practical to deploy since there is minimal computation done during the
actual attack. Several techniques have also been proposed to defend the neural
networks against these perturbations. In this paper, we attempt to provide a
detailed discussion on the various data-driven and data-independent methods for
generating universal perturbations, along with measures to defend against such
perturbations. We also cover the applications of such universal perturbations
in various deep learning tasks.
