Deflecting Adversarial Attacks with Pixel Deflection

TL;DR

This paper introduces Pixel Deflection, a simple yet effective image processing technique that significantly improves CNN robustness against adversarial attacks by redistributing pixel values and applying wavelet denoising, without retraining the model.

Contribution

The paper proposes a novel pixel deflection algorithm combined with wavelet denoising to defend CNNs from adversarial attacks without retraining or modifying the network.

Findings

Effective recovery of true class in adversarial examples

Outperforms current state-of-the-art defenses

Does not require retraining or model modification

Abstract

CNNs are poised to become integral parts of many critical systems. Despite their robustness to natural variations, image pixel values can be manipulated, via small, carefully crafted, imperceptible perturbations, to cause a model to misclassify images. We present an algorithm to process an image so that classification accuracy is significantly preserved in the presence of such adversarial manipulations. Image classifiers tend to be robust to natural noise, and adversarial attacks tend to be agnostic to object location. These observations motivate our strategy, which leverages model robustness to defend against adversarial perturbations by forcing the image to match natural image statistics. Our algorithm locally corrupts the image by redistributing pixel values via a process we term pixel deflection. A subsequent wavelet-based denoising operation softens this corruption, as well as some of the adversarial changes. We demonstrate experimentally that the combination of these techniques enables the effective recovery of the true class, against a variety of robust attacks. Our results compare favorably with current state-of-the-art defenses, without requiring retraining or modifying the CNN.