  Deep neural networks (DNNs) are now commonly used in many domains. However,
they are vulnerable to adversarial attacks: carefully crafted perturbations on
data inputs that can fool a model into making incorrect predictions. Despite
significant research on developing DNN attack and defense techniques, people
still lack an understanding of how such attacks penetrate a model's internals.
We present Bluff, an interactive system for visualizing, characterizing, and
deciphering adversarial attacks on vision-based neural networks. Bluff allows
people to flexibly visualize and compare the activation pathways for benign and
attacked images, revealing mechanisms that adversarial attacks employ to
inflict harm on a model. Bluff is open-sourced and runs in modern web browsers.
