Geometric Robustness of Deep Networks: Analysis and Improvement

Can Kanbak, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 4441-4449

Abstract


Deep convolutional neural networks have been shown to be vulnerable to arbitrary geometric transformations. However, there is no systematic method to measure the invariance properties of deep networks to such transformations. We propose ManiFool as a simple yet scalable algorithm to measure the invariance of deep networks. In particular, our algorithm measures the robustness of deep networks to geometric transformations in a worst-case regime as they can be problematic for sensitive applications. Our extensive experimental results show that ManiFool can be used to measure the invariance of fairly complex networks on high dimensional datasets and these values can be used for analyzing the reasons for it. Furthermore, we build on ManiFool to propose a new adversarial training scheme and we show its effectiveness on improving the invariance properties of deep neural networks.

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[bibtex]
@InProceedings{Kanbak_2018_CVPR,
author = {Kanbak, Can and Moosavi-Dezfooli, Seyed-Mohsen and Frossard, Pascal},
title = {Geometric Robustness of Deep Networks: Analysis and Improvement},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}