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DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses
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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.
Forward citations
Cited by 3 Pith papers
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GRAND : Graph Reconstruction from potential partial Adjacency and Neighborhood Data
An attacker given only the common-neighbors matrix of a graph can rebuild the graph up to co-square equivalence, and knowing a few edges makes the rebuild more accurate.
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REAL-IoT: Characterizing GNN Intrusion Detection Robustness under Practical Adversarial Attack
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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On the Adversarial Robustness of Graph Neural Networks with Graph Reduction
Graph sparsification mitigates some GNN poisoning attacks, while graph coarsening tends to amplify them, though the study lacks a clean-reduction baseline to fully support this.
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