DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses

TL;DR

DeepRobust is a comprehensive PyTorch library that provides a wide range of adversarial attack and defense algorithms for image and graph deep learning models, facilitating research in adversarial robustness.

Contribution

It offers an extensive, easy-to-use platform with over 10 attack and 8 defense algorithms for images, and 9 attack and 4 defense algorithms for graphs, supporting various architectures.

Findings

Supports multiple attack and defense algorithms

Covers both image and graph domains

Facilitates research in adversarial robustness

Abstract

DeepRobust is a PyTorch adversarial learning library which aims to build a comprehensive and easy-to-use platform to foster this research field. It currently contains more than 10 attack algorithms and 8 defense algorithms in image domain and 9 attack algorithms and 4 defense algorithms in graph domain, under a variety of deep learning architectures. In this manual, we introduce the main contents of DeepRobust with detailed instructions. The library is kept updated and can be found at https://github.com/DSE-MSU/DeepRobust.