DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses

Abstract

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.

Adversarial attacks and defenses, Pytorch library

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Cited by in corpus (21)

• A Comprehensive Survey on Deep Graph Representation Learning
• GNNGuard: Defending Graph Neural Networks against Adversarial Attacks
• RobustBench: a standardized adversarial robustness benchmark
• Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art
• Graph Structure Learning for Robust Graph Neural Networks
• Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies
• Robust Mid-Pass Filtering Graph Convolutional Networks
• Graph Neural Networks: Methods, Applications, and Opportunities
• Towards Robust Graph Contrastive Learning
• Node Injection for Class-specific Network Poisoning
• Hybrid Classical-Quantum Deep Learning Models for Autonomous Vehicle Traffic Image Classification Under Adversarial Attack
• Simple and Efficient Partial Graph Adversarial Attack: A New Perspective
• Node Similarity Preserving Graph Convolutional Networks
• Graph Neural Networks Inspired by Classical Iterative Algorithms
• Jointly Attacking Graph Neural Network and its Explanations
• Black-box Gradient Attack on Graph Neural Networks: Deeper Insights in Graph-based Attack and Defense
• SEC4SR: A Security Analysis Platform for Speaker Recognition
• I-GCN: Robust Graph Convolutional Network via Influence Mechanism
• Detecting Adversaries, yet Faltering to Noise? Leveraging Conditional Variational AutoEncoders for Adversary Detection in the Presence of Noisy Images
• Spatio-Temporal Sparsification for General Robust Graph Convolution Networks
• CoG: a Two-View Co-training Framework for Defending Adversarial Attacks on Graph