Hilbert-Based Generative Defense for Adversarial Examples

Yang Bai, Yan Feng, Yisen Wang, Tao Dai, Shu-Tao Xia, Yong Jiang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 4784-4793


Adversarial perturbations of clean images are usually imperceptible for human eyes, but can confidently fool deep neural networks (DNNs) to make incorrect predictions. Such vulnerability of DNNs raises serious security concerns about their practicability in security-sensitive applications. To defend against such adversarial perturbations, recently developed PixelDefend purifies a perturbed image based on PixelCNN in a raster scan order (row/column by row/column). However, such scan mode insufficiently exploits the correlations between pixels, which further limits its robustness performance. Therefore, we propose a more advanced Hilbert curve scan order to model the pixel dependencies in this paper. Hilbert curve could well preserve local consistency when mapping from 2-D image to 1-D vector, thus the local features in neighboring pixels can be more effectively modeled. Moreover, the defensive power can be further improved via ensembles of Hilbert curve with different orientations. Experimental results demonstrate the superiority of our method over the state-of-the-art defenses against various adversarial attacks.

Related Material

author = {Bai, Yang and Feng, Yan and Wang, Yisen and Dai, Tao and Xia, Shu-Tao and Jiang, Yong},
title = {Hilbert-Based Generative Defense for Adversarial Examples},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}