# A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations

## TL;DR

This paper introduces a non-linear manifold mapping technique using kernelized features to enhance the robustness of deep convolutional neural networks against adversarial attacks without sacrificing accuracy on clean data.

## Contribution

It proposes a novel radial basis convolutional feature mapping that maps features onto a well-separated manifold, reducing adversarial vulnerability.

## Key findings

- Improves robustness to gradient and non-gradient attacks
- Maintains accuracy on clean datasets
- Outperforms several masking defense strategies

## Abstract

The linear and non-flexible nature of deep convolutional models makes them vulnerable to carefully crafted adversarial perturbations. To tackle this problem, we propose a non-linear radial basis convolutional feature mapping by learning a Mahalanobis-like distance function. Our method then maps the convolutional features onto a linearly well-separated manifold, which prevents small adversarial perturbations from forcing a sample to cross the decision boundary. We test the proposed method on three publicly available image classification and segmentation datasets namely, MNIST, ISBI ISIC 2017 skin lesion segmentation, and NIH Chest X-Ray-14. We evaluate the robustness of our method to different gradient (targeted and untargeted) and non-gradient based attacks and compare it to several non-gradient masking defense strategies. Our results demonstrate that the proposed method can increase the resilience of deep convolutional neural networks to adversarial perturbations without accuracy drop on clean data.