DSE-MSU/DeepRobust

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1085 stars · 192 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

DeepRobust is a PyTorch library for adversarial robustness research, providing implementations of attack and defense methods for both image classifiers and graph neural networks. It is aimed at researchers benchmarking adversarial examples and defenses in machine learning.

Use cases

• generate adversarial examples against image classifiers
• evaluate robustness of deep neural networks
• run adversarial attacks on graph neural networks
• test graph poisoning and evasion defenses
• benchmark attack and defense algorithms in PyTorch
• research adversarial machine learning on images and graphs

When to choose

• you need ready-made adversarial attack and defense implementations in PyTorch
• you study robustness of both image models and graph neural networks
• you want a benchmarking baseline library for adversarial ML research

When to avoid

• you need production-grade model security rather than research tooling
• you work in TensorFlow or another framework instead of PyTorch
• you only need general deep learning utilities without adversarial focus

Facets

library · maturity active

machine-learning deep-learning security benchmarking machine-learning deep-learning security computer-vision python adversarial-attacks adversarial-defense adversarial-robustness graph-neural-networks pytorch adversarial-examples

Member repositories

For agents

markdown · JSON · MCP: product_card(name="DSE-MSU/DeepRobust")