mlsanityData to 7 Oct 2026

DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses

arXiv 2005.06149 ↗First posted 13 May 2020cs.LG cs.CR stat.MLcited by 78

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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.

Key facts. Cited by 78 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 May 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 18; 2022: 21; 2023: 12; 2024: 16; 2025: 6; 2026: 5.

78 papers.

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  1. 2021Trustworthy AI: A Computational Perspectivecited by 93
  2. 2021Elastic Graph Neural Networkscited by 67
  3. 2022Empowering Graph Representation Learning with Test-Time Graph Transformationcited by 66
  4. 2022Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labelscited by 63
  5. 2021Graph Neural Networks Inspired by Classical Iterative Algorithmscited by 60
  6. 2021Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learningcited by 44
  7. 2023Are Defenses for Graph Neural Networks Robust?cited by 38
  8. 2022Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNNcited by 34
  9. 2022DropMessage: Unifying Random Dropping for Graph Neural Networkscited by 29
  10. 2022EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networkscited by 26
  11. 2023Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approachcited by 24
  12. 2024Graph Condensation: A Surveycited by 23
38 more
  1. 2024Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?cited by 22
  2. 2021How does Heterophily Impact the Robustness of Graph Neural Networks? Theoretical Connections and Practical Implicationscited by 21
  3. 2022Adversarial Weight Perturbation Improves Generalization in Graph Neural Networkscited by 21
  4. 2022Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinforcement Learningcited by 19
  5. 2023Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspectivecited by 19
  6. 2021Jointly Attacking Graph Neural Network and its Explanationscited by 18
  7. 2021Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Artcited by 18
  8. 2023Robust Mid-Pass Filtering Graph Convolutional Networkscited by 17
  9. 2021Towards Robust Graph Contrastive Learningcited by 16
  10. 2021Graph Neural Networks: Methods, Applications, and Opportunitiescited by 16
  11. 2021Graph Structural Attack by Perturbing Spectral Distancecited by 16
  12. 2024Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FRONDcited by 16
  13. 2022GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networkscited by 14
  14. 2024AttackBench: Evaluating Gradient-based Attacks for Adversarial Examplescited by 14
  15. 2021Implicit vs Unfolded Graph Neural Networkscited by 12
  16. 2022Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networkscited by 12
  17. 2024A Simple and Yet Fairly Effective Defense for Graph Neural Networkscited by 12
  18. 2021Imbalanced Adversarial Training with Reweightingcited by 11
  19. 2024Learning on Graphs with Large Language Models(LLMs): A Deep Dive into Model Robustnesscited by 11
  20. 2022Curvature Graph Generative Adversarial Networkscited by 9
  21. 2024Coupling Graph Neural Networks with Fractional Order Continuous Dynamics: A Robustness Studycited by 9
  22. 2023Simple and Efficient Partial Graph Adversarial Attack: A New Perspectivecited by 8
  23. 2023Adversarially Robust Neural Architecture Search for Graph Neural Networkscited by 7
  24. 2024GC4NC: A Benchmark Framework for Graph Condensation on Node Classification with New Insightscited by 7
  25. 2022Towards Adversarial Purification using Denoising AutoEncoderscited by 6
  26. 2023Similarity Preserving Adversarial Graph Contrastive Learningcited by 6
  27. 2021Hybrid Classical-Quantum Deep Learning Models for Autonomous Vehicle Traffic Image Classification Under Adversarial Attackcited by 5
  28. 2022Robust Graph Representation Learning via Predictive Codingcited by 5
  29. 2023Node Injection for Class-specific Network Poisoningcited by 5
  30. 2022Robust Graph Neural Networks using Weighted Graph Laplaciancited by 4
  31. 2023IDEA: Invariant Defense for Graph Adversarial Robustnesscited by 4
  32. 2026Are LLM-Enhanced Graph Neural Networks Robust against Poisoning Attacks?cited by 4
  33. 2021Black-box Gradient Attack on Graph Neural Networks: Deeper Insights in Graph-based Attack and Defensecited by 3
  34. 2021SEC4SR: A Security Analysis Platform for Speaker Recognitioncited by 3
  35. 2022An integrated Auto Encoder-Block Switching defense approach to prevent adversarial attackscited by 3
  36. 2022Spiking GATs: Learning Graph Attentions via Spiking Neural Networkcited by 3
  37. 2023Single Node Injection Label Specificity Attack on Graph Neural Networks via Reinforcement Learningcited by 3
  38. 2023Everything Perturbed All at Once: Enabling Differentiable Graph Attackscited by 2

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