Defending Against Adversarial Attacks Using Random Forests
This addresses security vulnerabilities in AI systems, offering a defense mechanism against adversarial manipulations, though it is incremental as it builds on existing methods.
The paper tackles the problem of adversarial attacks on deep neural networks by proposing a hybrid model combining DNNs and random forests, which successfully defends against white-box attacks and resists black-box attacks while maintaining similar classification accuracy.
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 the 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.