Adversarial-Playground- A Visualization Suite for Adversarial Sample Generation

Paper Arxiv

GitHub: AdversarialDNN-Playground

Poster

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

@inproceedings{norton2017adversarial,
  title={Adversarial-Playground: A visualization suite showing how adversarial examples fool deep learning},
  author={Norton, Andrew P and Qi, Yanjun},
  booktitle={Visualization for Cyber Security (VizSec), 2017 IEEE Symposium on},
  pages={1--4},
  year={2017},
  organization={IEEE}
}

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