Skip to content

A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees

License

Notifications You must be signed in to change notification settings

TrustAI/DeepGame

Repository files navigation

DeepGame (A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees)

Min Wu, Matthew Wicker, Wenjie Ruan, Xiaowei Huang, Marta Kwiatkowska.

The accompanying paper A game-based approximate verification of deep neural networks with provable guarantees is accepted by Theoretical Computer Science and available online since 2019.

Citation

@article{wu2020game,
  title   = "A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees",
  author  = "Wu, Min and Wicker, Matthew and Ruan, Wenjie and Huang, Xiaowei and Kwiatkowska, Marta",
  journal = "Theoretical Computer Science",
  volume  = "807",
  pages   = "298 - 329",
  year    = "2020",
  note    = "In memory of Maurice Nivat, a founding father of Theoretical Computer Science - Part II",
  issn    = "0304-3975",
  doi     = "https://doi.org/10.1016/j.tcs.2019.05.046",
  url     = "http://www.sciencedirect.com/science/article/pii/S0304397519304426"
}

Abstract

Despite the improved accuracy of deep neural networks, the discovery of adversarial examples has raised serious safety concerns. In this paper, we study two variants of pointwise robustness, the maximum safe radius problem, which for a given input sample computes the minimum distance to an adversarial example, and the feature robustness problem, which aims to quantify the robustness of individual features to adversarial perturbations. We demonstrate that, under the assumption of Lipschitz continuity, both problems can be approximated using finite optimisation by discretising the input space, and the approximation has provable guarantees, i.e., the error is bounded. We then show that the resulting optimisation problems can be reduced to the solution of two-player turn-based games, where the first player selects features and the second perturbs the image within the feature. While the second player aims to minimise the distance to an adversarial example, depending on the optimisation objective the first player can be cooperative or competitive. We employ an anytime approach to solve the games, in the sense of approximating the value of a game by monotonically improving its upper and lower bounds. The Monte Carlo tree search algorithm is applied to compute upper bounds for both games, and the Admissible A* and the Alpha-Beta Pruning algorithms are, respectively, used to compute lower bounds for the maximum safety radius and feature robustness games. When working on the upper bound of the maximum safe radius problem, our tool demonstrates competitive performance against existing adversarial example crafting algorithms. Furthermore, we show how our framework can be deployed to evaluate pointwise robustness of neural networks in safety-critical applications such as traffic sign recognition in self-driving cars.

Problem Statement

alt text alt text

Approach Architecture

alt text alt text alt text

Convergence Results

alt text alt text alt text alt text alt text

Adversarial Examples

alt text alt text alt text alt text

Developer's Platform

python 3.5.5
keras 2.1.3
tensorflow-gpu 1.4.0
numpy 1.14.3
matplotlib 2.2.2
scipy 1.1.0

Run

python main.py mnist ub cooperative 67 L2 10 1

or

./commands.sh

Remark

This tool is under active development and maintenance, please feel free to contact us about any problem encountered.

Best wishes,

xiaowei.huang@cs.ox.ac.uk

min.wu@cs.ox.ac.uk

About

A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published