How to compare adversarial robustness of classifiers from a global perspective

Authors: Niklas Risse, Christina Göpfert and Jan Philip Göpfert

Institution: Bielefeld University

What is in this repository?

A python module to calculate robustness curves for arbitrary models and datasets
The code to reproduce all experiments (with figures) from the paper, including the following:
- 2 experiments on inter class distances for different datasets and norms
- 5 experiments on robustness curves for different models and datasets

Main idea of the paper

Adversarial robustness of machine learning models has attracted considerable attention over recent years. Adversarial attacks undermine the reliability of and trust in machine learning models, but the construction of more robust models hinges on a rigorous understanding of adversarial robustness as a property of a given model. Point-wise measures for specific threat models are currently the most popular tool for comparing the robustness of classifiers and are used in most recent publications on adversarial robustness. In this work, we use recently proposed robustness curves to show that point-wise measures fail to capture important global properties that are essential to reliably compare the robustness of different classifiers. We introduce new ways in which robustness curves can be used to systematically uncover these properties and provide concrete recommendations for researchers and practitioners when assessing and comparing the robustness of trained models. Furthermore, we characterize scale as a way to distinguish small and large perturbations, and relate it to inherent properties of data sets, demonstrating that robustness thresholds must be chosen accordingly.

How to generate robustness curves for arbitrary models and datasets

The python module robustness_curves.py contains methods to calculate robustness curves. A tutorial on how to use the module can be found in the notebook how_to_generate_robustness_curves.ipynb. Most of the popular machine learning frameworks are supported (e.g. TensorFlow, PyTorch, JAX). Datasets need to be in numpy array format. The module can be used to generate robustness curves for the l_infty, l_2 and l_1 norms. Plotting and/or saving the generated data is optional.

Installation and Setup

We manage python dependencies with anaconda. You can find information on how to install anaconda at: https://docs.anaconda.com/anaconda/install/. After installing, create the environment with executing conda env create in the root directory of the repository. This automatically finds and uses the file environment.yml, which creates an environment called robustness with everything needed to run our python files and notebooks. Activate the environment with conda activate robustness.

We use tensorflow-gpu 2.1 to calculate adversarial examples. To correctly set up tensorflow for your GPU, follow the instructions from: https://www.tensorflow.org/install/gpu.

The models and datasets for the experiments are hosted in Google Drive by Croce et al.. In order to reproduce the experiments, you need to download them and extract the folders models and datasets into the submodule directory provable_robustness_max_linear_regions.

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