Defending Against Adversarial Attacks Using Random Forest

Yifan Ding, Liqiang Wang, Huan Zhang, Jinfeng Yi, Deliang Fan, Boqing Gong; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0


As deep neural networks (DNNs) have become increasingly important and popular, the robustness of DNNs is the key to the safety of both the Internet and physical world. Unfortunately, some recent studies show that adversarial examples, which are hard to be distinguished from real examples, can easily fool DNNs and manipulate their predictions. Upon observing that adversarial examples are mostly generated by gradient-based methods, in this paper, we first propose to use a simple yet very effective non-differentiable hybrid model that combines DNNs and random forests, rather than hide gradients from attackers, to defend against the attacks. Our experiments show that our model can successfully and completely defend the white-box attacks, has a lower transferability, and is quite resistant to three representative types of black-box attacks; while at the same time, our model achieves similar classification accuracy as the original DNNs. Finally, we investigate and suggest a criterion to define where to grow random forests in DNNs.

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[pdf] [dataset]
author = {Ding, Yifan and Wang, Liqiang and Zhang, Huan and Yi, Jinfeng and Fan, Deliang and Gong, Boqing},
title = {Defending Against Adversarial Attacks Using Random Forest},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}