  As neural networks become the tool of choice to solve an increasing variety
of problems in our society, adversarial attacks become critical. The
possibility of generating data instances deliberately designed to fool a
network's analysis can have disastrous consequences. Recent work has shown that
commonly used methods for model training often result in fragile abstract
representations that are particularly vulnerable to such attacks. This paper
presents a visual framework to investigate neural network models subjected to
adversarial examples, revealing how models' perception of the adversarial data
differs from regular data instances and their relationships with class
perception. Through different use cases, we show how observing these elements
can quickly pinpoint exploited areas in a model, allowing further study of
vulnerable features in input data and serving as a guide to improving model
training and architecture.
