1 code implementation • 31 Mar 2018 • Alexey Kurakin, Ian Goodfellow, Samy Bengio, Yinpeng Dong, Fangzhou Liao, Ming Liang, Tianyu Pang, Jun Zhu, Xiaolin Hu, Cihang Xie, Jian-Yu Wang, Zhishuai Zhang, Zhou Ren, Alan Yuille, Sangxia Huang, Yao Zhao, Yuzhe Zhao, Zhonglin Han, Junjiajia Long, Yerkebulan Berdibekov, Takuya Akiba, Seiya Tokui, Motoki Abe
To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop new methods to generate adversarial examples as well as to develop new ways to defend against them.
2 code implementations • CVPR 2018 • Fangzhou Liao, Ming Liang, Yinpeng Dong, Tianyu Pang, Xiaolin Hu, Jun Zhu
First, with HGD as a defense, the target model is more robust to either white-box or black-box adversarial attacks.
11 code implementations • 22 Nov 2017 • Fangzhou Liao, Ming Liang, Zhe Li, Xiaolin Hu, Sen Song
The model consists of two modules.
7 code implementations • CVPR 2018 • Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, Jianguo Li
To further improve the success rates for black-box attacks, we apply momentum iterative algorithms to an ensemble of models, and show that the adversarially trained models with a strong defense ability are also vulnerable to our black-box attacks.
1 code implementation • 13 Feb 2017 • Fangzhou Liao, Xi Chen, Xiaolin Hu, Sen Song
In 2016, Kaggle organized a competition to estimate the volume of LV from MRI images.