4-Visualization

This category of tools aims to enable machine learning practitioners and users to understand how machine-learning models may be attacked..
This includes:


We have revised the Adversarial-Playground Toolbox for Visualizing Adversarial Examples

Adversarial-Playground: A Visualization Suite for Adversarial Sample Generation

Revised Version2 Paper Arxiv

Abstract

Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep Learning

Recent studies have shown that attackers can force deep learning models to misclassify so-called “adversarial examples”: maliciously generated images formed by making imperceptible modifications to pixel values. With growing interest in deep learning for security applications, it is important for security experts and users of machine learning to recognize how learning systems may be attacked. Due to the complex nature of deep learning, it is challenging to understand how deep models can be fooled by adversarial examples. Thus, we present a web-based visualization tool, Adversarial-Playground, to demonstrate the efficacy of common adversarial methods against a convolutional neural network (CNN) system. Adversarial-Playground is educational, modular and interactive. (1) It enables non-experts to compare examples visually and to understand why an adversarial example can fool a CNN-based image classifier. (2) It can help security experts explore more vulnerability of deep learning as a software module. (3) Building an interactive visualization is challenging in this domain due to the large feature space of image classification (generating adversarial examples is slow in general and visualizing images are costly). Through multiple novel design choices, our tool can provide fast and accurate responses to user requests. Empirically, we find that our client-server division strategy reduced the response time by an average of 1.5 seconds per sample. Our other innovation, a faster variant of JSMA evasion algorithm, empirically performed twice as fast as JSMA and yet maintains a comparable evasion rate. Project source code and data from our experiments available at: GitHub

Citations

@article{norton2017advplayground,
  title={Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep Learning},
  author={Norton, Andrew and Qi, Yanjun},
  url = {http://arxiv.org/abs/1708.00807}
  year={2017},
}

Support or Contact

Having trouble with our tools? Please contact Andrew Norton and we’ll help you sort it out.


A Toolbox for Visualizing Adversarial Examples

Adversarial-Playground: A Visualization Suite for Adversarial Sample Generation

GitHub: AdversarialDNN-Playground

Paper Arxiv

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.

Playground

pg pg

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},
}

Support or Contact

Having trouble with our tools? Please contact Andrew Norton and we’ll help you sort it out.