# Defending Against Adversarial Attacks Using Random Forests

## TL;DR

This paper proposes a hybrid model combining DNNs and random forests to defend against adversarial attacks, effectively resisting various attack types while maintaining classification accuracy.

## Contribution

It introduces a non-differentiable hybrid model that leverages random forests to improve robustness against adversarial attacks without sacrificing accuracy.

## Key findings

- Successfully defends against white-box attacks
- Reduces transferability of adversarial examples
- Maintains similar accuracy to original DNNs

## Abstract

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.