Deep learning has been a popular topic and has achieved success in many
areas. It has drawn the attention of researchers and machine learning
practitioners alike, with developed models deployed to a variety of settings.
Along with its achievements, research has shown that deep learning models are
vulnerable to adversarial attacks. This finding brought about a new direction
in research, whereby algorithms were developed to attack and defend vulnerable
networks. Our interest is in understanding how these attacks effect change on
the intermediate representations of deep learning models. We present a method
for measuring and analyzing the deviations in representations induced by
adversarial attacks, progressively across a selected set of layers. Experiments
are conducted using an assortment of attack algorithms, on the CIFAR-10
dataset, with plots created to visualize the impact of adversarial attacks
across different layers in a network