  With growing interest in adversarial machine learning, it is important for
machine learning practitioners and users to understand how their models may be
attacked. We propose a web-based visualization tool, Adversarial-Playground, to
demonstrate the efficacy of common adversarial methods against a deep neural
network (DNN) model, built on top of the TensorFlow library.
Adversarial-Playground provides users an efficient and effective experience in
exploring techniques generating adversarial examples, which are inputs crafted
by an adversary to fool a machine learning system. To enable
Adversarial-Playground to generate quick and accurate responses for users, we
use two primary tactics: (1) We propose a faster variant of the
state-of-the-art Jacobian saliency map approach that maintains a comparable
evasion rate. (2) Our visualization does not transmit the generated adversarial
images to the client, but rather only the matrix describing the sample and the
vector representing classification likelihoods.
  The source code along with the data from all of our experiments are available
at \url{https://github.com/QData/AdversarialDNN-Playground}.
