# A Toolbox for Visualizing Adversarial Examples

## Adversarial-Playground: A Visualization Suite for Adversarial Sample Generation

### Abstract

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, \textit{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.

### Citations

@article{norton2017advplayground,
title={Adversarial Playground: A Visualization Suite for Adversarial Sample Generation},
author={Norton, Andrew and Qi, Yanjun},
url = {http://arxiv.org/abs/1706.01763}
year={2017},
}