Certified Patch Robustness via Smoothed Vision Transformers

Hadi Salman, Saachi Jain, Eric Wong, Aleksander Madry; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 15137-15147

Abstract

Certified patch defenses can guarantee robustness of an image classifier to arbitrary changes within a bounded contiguous region. But, currently, this robustness comes at a cost of degraded standard accuracies and slower inference times. We demonstrate how using vision transformers enables significantly better certified patch robustness that is also more computationally efficient and does not incur a substantial drop in standard accuracy. These improvements stem from the inherent ability of the vision transformer to gracefully handle largely masked images.

Related Material

[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Salman_2022_CVPR, author = {Salman, Hadi and Jain, Saachi and Wong, Eric and Madry, Aleksander}, title = {Certified Patch Robustness via Smoothed Vision Transformers}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {15137-15147} }