TY - JOUR AU - Li, Yaxin AU - Jin, Wei AU - Xu, Han AU - Tang, Jiliang PY - 2021/05/18 Y2 - 2024/03/29 TI - DeepRobust: a Platform for Adversarial Attacks and Defenses JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 35 IS - 18 SE - AAAI Demonstration Track DO - 10.1609/aaai.v35i18.18017 UR - https://ojs.aaai.org/index.php/AAAI/article/view/18017 SP - 16078-16080 AB - 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 DeepRobust and get performance analyzing visualization. In this paper, we introduce the functions of DeepRobust with detailed instructions. We believe that DeepRobust is a useful tool to measure deep learning model robustness and to find 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 instruction can be found in the documentation at https://deeprobust.readthedocs.io/en/latest/. ER -