# DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses

arXiv: https://arxiv.org/abs/2005.06149 · HTML: https://mlsanity.com/paper/2005.06149/

## Key facts

- Cited by 78 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2020.

## Built on by (most cited first, 30 of 78)

- [Trustworthy AI: A Computational Perspective](https://mlsanity.com/paper/2107.06641/) (2021)
- [Elastic Graph Neural Networks](https://mlsanity.com/paper/2107.06996/) (2021)
- [Empowering Graph Representation Learning with Test-Time Graph Transformation](https://mlsanity.com/paper/2210.03561/) (2022)
- [Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels](https://mlsanity.com/paper/2201.00232/) (2022)
- [Graph Neural Networks Inspired by Classical Iterative Algorithms](https://mlsanity.com/paper/2103.06064/) (2021)
- [Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning](https://mlsanity.com/paper/2111.04314/) (2021)
- [Are Defenses for Graph Neural Networks Robust?](https://mlsanity.com/paper/2301.13694/) (2023)
- [Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN](https://mlsanity.com/paper/2207.00012/) (2022)
- [DropMessage: Unifying Random Dropping for Graph Neural Networks](https://mlsanity.com/paper/2204.10037/) (2022)
- [EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks](https://mlsanity.com/paper/2205.13892/) (2022)
- [Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach](https://mlsanity.com/paper/2310.06396/) (2023)
- [Graph Condensation: A Survey](https://mlsanity.com/paper/2401.11720/) (2024)
- [Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?](https://mlsanity.com/paper/2408.08685/) (2024)
- [How does Heterophily Impact the Robustness of Graph Neural Networks? Theoretical Connections and Practical Implications](https://mlsanity.com/paper/2106.07767/) (2021)
- [Adversarial Weight Perturbation Improves Generalization in Graph Neural Networks](https://mlsanity.com/paper/2212.04983/) (2022)
- [Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective](https://mlsanity.com/paper/2302.09457/) (2023)
- [Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinforcement Learning](https://mlsanity.com/paper/2211.10782/) (2022)
- [Jointly Attacking Graph Neural Network and its Explanations](https://mlsanity.com/paper/2108.03388/) (2021)
- [Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art](https://mlsanity.com/paper/2112.12310/) (2021)
- [Robust Mid-Pass Filtering Graph Convolutional Networks](https://mlsanity.com/paper/2302.08048/) (2023)
- [Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND](https://mlsanity.com/paper/2404.17099/) (2024)
- [Towards Robust Graph Contrastive Learning](https://mlsanity.com/paper/2102.13085/) (2021)
- [Graph Neural Networks: Methods, Applications, and Opportunities](https://mlsanity.com/paper/2108.10733/) (2021)
- [Graph Structural Attack by Perturbing Spectral Distance](https://mlsanity.com/paper/2111.00684/) (2021)
- [GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks](https://mlsanity.com/paper/2201.12741/) (2022)
- [AttackBench: Evaluating Gradient-based Attacks for Adversarial Examples](https://mlsanity.com/paper/2404.19460/) (2024)
- [Implicit vs Unfolded Graph Neural Networks](https://mlsanity.com/paper/2111.06592/) (2021)
- [Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networks](https://mlsanity.com/paper/2209.05957/) (2022)
- [A Simple and Yet Fairly Effective Defense for Graph Neural Networks](https://mlsanity.com/paper/2402.13987/) (2024)
- [Imbalanced Adversarial Training with Reweighting](https://mlsanity.com/paper/2107.13639/) (2021)

## Abstract (arXiv)

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.