Panda or not Panda? Understanding Adversarial Attacks with Interactive
  Visualization
Yuzhe You Jarvis Tse Jian Zhao
Adversarial machine learning (AML) studies attacks that can fool machine
learning algorithms into generating incorrect outcomes as well as the defenses
against worst-case attacks to strengthen model robustness. Specifically for
image classification, it is challenging to understand adversarial attacks due
to their use of subtle perturbations that are not human-interpretable, as well
as the variability of attack impacts influenced by diverse methodologies,
instance differences, and model architectures. Through a design study with AML
learners and teachers, we introduce AdvEx, a multi-level interactive
visualization system that comprehensively presents the properties and impacts
of evasion attacks on different image classifiers for novice AML learners. We
quantitatively and qualitatively assessed AdvEx in a two-part evaluation
including user studies and expert interviews. Our results show that AdvEx is
not only highly effective as a visualization tool for understanding AML
mechanisms, but also provides an engaging and enjoyable learning experience,
thus demonstrating its overall benefits for AML learners.