Feature Space Perturbations Yield More Transferable Adversarial Examples

Nathan Inkawhich, Wei Wen, Hai (Helen) Li, Yiran Chen; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 7066-7074

Abstract


Many recent works have shown that deep learning models are vulnerable to quasi-imperceptible input perturbations, yet practitioners cannot fully explain this behavior. This work describes a transfer-based blackbox targeted adversarial attack of deep feature space representations that also provides insights into cross-model class representations of deep CNNs. The attack is explicitly designed for transferability and drives feature space representation of a source image at layer L towards the representation of a target image at L. The attack yields highly transferable targeted examples, which outperform competition winning methods by over 30% in targeted attack metrics. We also show the choice of L to generate examples from is important, transferability characteristics are blackbox model agnostic, and indicate that well trained deep models have similar highly-abstract representations.

Related Material


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[bibtex]
@InProceedings{Inkawhich_2019_CVPR,
author = {Inkawhich, Nathan and Wen, Wei and (Helen) Li, Hai and Chen, Yiran},
title = {Feature Space Perturbations Yield More Transferable Adversarial Examples},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}