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Paper · 2005.06149 · 2020

DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses

arXiv · PDF · Open in the Atlas

Code that ran

We lifted 10 functions out of this paper's own repositories and ran 6 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.

RepositoryRoleRan
DSE-MSU/DeepRobust canonical 1 of 1
I-am-Bot/DeepRobust pwc_unofficial 5 of 9
FunctionStatusWhere it lives
connected_after Ran I-am-Bot/DeepRobust/deeprobust/graph/targeted_attack/nettack.py
code served (permissive licence) · get_code("e76a55dd6370c052")
encode_onehot Ran I-am-Bot/DeepRobust/deeprobust/graph/utils.py
code served (permissive licence) · get_code("834ca5c476a3f197")
filter_singletons Ran I-am-Bot/DeepRobust/deeprobust/graph/targeted_attack/nettack.py
code served (permissive licence) · get_code("584a4ebd7ec447c3")
preprocess Ran I-am-Bot/DeepRobust/deeprobust/graph/utils.py
code served (permissive licence) · get_code("1007eb388147d7c5")
proj_lp Ran DSE-MSU/DeepRobust/deeprobust/image/attack/Universal.py
code served (permissive licence) · get_code("f800c8aa4f28200b")
tensor2onehot Ran I-am-Bot/DeepRobust/deeprobust/graph/utils.py
code served (permissive licence) · get_code("edde8b8b03e59989")
compute_new_a_hat_uv Not yet run I-am-Bot/DeepRobust/deeprobust/graph/targeted_attack/nettack.py
code served (permissive licence) · get_code("301bb3fd39ebe688")
generate_dataloader Not yet run I-am-Bot/DeepRobust/deeprobust/image/evaluation_attack.py
code served (permissive licence) · get_code("3a260a14cb5e6796")
load_net Not yet run I-am-Bot/DeepRobust/deeprobust/image/evaluation_attack.py
code served (permissive licence) · get_code("055d12d90bb8de9b")
node_greedy_actions Not yet run I-am-Bot/DeepRobust/deeprobust/graph/rl/nipa_q_net_node.py
code served (permissive licence) · get_code("b206a8df7d4cb8c5")

Repositories linked to this paper

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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.

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