DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses

Han Xu; Jiliang Tang; Wei Jin; Yaxin Li

DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses

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arxiv 2005.06149 v1 pith:XKJDEQ7M submitted 2020-05-13 cs.LG cs.CRstat.ML

Yaxin Li , Wei Jin , Han Xu , Jiliang Tang This is my paper

classification cs.LGcs.CRstat.ML

keywords algorithmsdeeprobustlibraryadversarialattackdefensedomainlearning

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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

REAL-IoT: Characterizing GNN Intrusion Detection Robustness under Practical Adversarial Attack
cs.CR 2025-07 reject novelty 5.0 of 10

GNN-based intrusion detectors show lower accuracy on REAL-IoT's merged datasets, but the paper's own tables are inconsistent and the drift protocol is not a true distribution-shift test.

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