Adversarial-Playground: A Visualization Suite for Adversarial Sample Generation

TL;DR

Adversarial-Playground is a web-based visualization tool that demonstrates how common adversarial attack methods can fool deep neural networks, providing an interactive platform for understanding adversarial sample generation.

Contribution

It introduces a fast, efficient variant of the Jacobian saliency map approach and a privacy-preserving visualization method for adversarial examples.

Findings

Achieves comparable evasion rates with faster adversarial sample generation.

Provides real-time visualization without transmitting adversarial images.

Enhances understanding of adversarial attacks through interactive exploration.

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, 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}.

Full text

See pages - of andrew2017_advplayground.pdf