DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses
This provides a tool for researchers in adversarial machine learning, but it is incremental as it builds on existing algorithms without introducing new methods.
The authors tackled the need for a comprehensive and easy-to-use platform for adversarial learning research by developing DeepRobust, a PyTorch library that includes over 10 attack and 8 defense algorithms for images and 9 attack and 4 defense algorithms for graphs.
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.