Name
Abstract | ||
|---|---|---|
DeepRobust is a PyTorch platform for generating adversarial examples and building robust machine learning models for different data domains. Users can easily evaluate the attack performance against different defense methods with Deep-Robust. In this paper, we introduce the functions of Deep-Robust with detailed instructions. We will demonstrate that DeepRobust is a useful tool to measure deep learning model robustness and to identify the suitable countermeasures against adversarial attacks. The platform is kept updated and can be found at https://github.com/DSE-MSU/DeepRobust. More details of instructions can be found in the documentation at https://deeprobust.readthedocs.io/en/latest/. |
Year | Venue | DocType |
|---|---|---|
2021 | THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | Conference |
Volume | ISSN | Citations |
35 | 2159-5399 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yaxin Li | 1 | 2 | 2.76 |
| Wei Jin | 2 | 13 | 2.25 |
| Han Xu | 3 | 2 | 1.41 |
| Jiliang Tang | 4 | 3323 | 140.81 |