Unifying Bilateral Filtering and Adversarial Training for Robust Neural Networks
This work addresses the problem of adversarial attacks in neural networks for applications requiring security, but it is incremental as it builds on existing filtering and training methods.
The paper tackles the vulnerability of deep neural networks to adversarial examples by using bilateral filtering as a defense, achieving over 90% removal of adversarial examples in some settings and enhancing robustness when integrated into adversarial training.
Recent analysis of deep neural networks has revealed their vulnerability to carefully structured adversarial examples. Many effective algorithms exist to craft these adversarial examples, but performant defenses seem to be far away. In this work, we explore the use of edge-aware bilateral filtering as a projection back to the space of natural images. We show that bilateral filtering is an effective defense in multiple attack settings, where the strength of the adversary gradually increases. In the case of an adversary who has no knowledge of the defense, bilateral filtering can remove more than 90% of adversarial examples from a variety of different attacks. To evaluate against an adversary with complete knowledge of our defense, we adapt the bilateral filter as a trainable layer in a neural network and show that adding this layer makes ImageNet images significantly more robust to attacks. When trained under a framework of adversarial training, we show that the resulting model is hard to fool with even the best attack methods.