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SmoothFool: An Efficient Framework for Computing Smooth Adversarial Perturbations
[article]
2019
arXiv
pre-print
Preserved Fulltext
In this paper, we study the smoothness of perturbations and propose SmoothFool, a general and computationally efficient framework for computing smooth adversarial perturbations. ...
In particular, we demonstrate that: (i) there exist extremely smooth adversarial perturbations for well-established and widely used network architectures, (ii) smoothness significantly enhances the robustness ...
Conclusion In this study, we explored the vulnerability extent of DNNs to smooth adversarial perturbations by proposing SmoothFool, a framework for computing 2 -minimal smooth APs. ...
arXiv:1910.03624v1
fatcat:tt6633nnanebla2ym4bpwa2jzy
SmoothFool: An Efficient Framework for Computing Smooth Adversarial Perturbations
2020
2020 IEEE Winter Conference on Applications of Computer Vision (WACV)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In this paper, we study the smoothness of perturbations and propose Smooth-Fool, a general and computationally efficient framework for computing smooth adversarial perturbations. ...
In particular, we demonstrate that: (i) there exist extremely smooth adversarial perturbations for well-established and widely used network architectures, (ii) smoothness significantly enhances the robustness ...
Our main contributions are the followings: • We propose SmoothFool, a geometry inspired framework for computing smooth APs which exploits the topology of decision boundaries to find efficient APs. • We ...
doi:10.1109/wacv45572.2020.9093429
dblp:conf/wacv/DaboueiSTDN20
fatcat:wt7pvonl6reytl5btgrdbsjnsi
Towards Imperceptible Universal Attacks on Texture Recognition
[article]
2020
arXiv
pre-print
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A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
As part of our work, we find that limiting the perturbation's l_p norm in the spatial domain may not be a suitable way to restrict the perceptibility of universal adversarial perturbations for texture ...
Based on the fact that human perception is affected by local visual frequency characteristics, we propose a frequency-tuned universal attack method to compute universal perturbations in the frequency domain ...
SmoothFool: an efficient framework for computing smooth adversarial perturbations. ...
arXiv:2011.11957v1
fatcat:ye5pgqs6jjgfllcr6j62vhp22m
Rethinking the Backdoor Attacks' Triggers: A Frequency Perspective
[article]
2022
arXiv
pre-print
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Moreover, we show that the detector tuned over stronger smooth triggers can generalize well to unseen weak smooth triggers. ...
While backdoor attacks have been thoroughly investigated in the image domain from both attackers' and defenders' sides, an analysis in the frequency domain has been missing thus far. ...
Target Model for Evaluating the Smooth Trigger In Section 5.3, we evaluate the proposed smooth attack's attack efficiency on the CIFAR-10 and GTSRB dataset. ...
arXiv:2104.03413v4
fatcat:xogb3o6gbnefjl2b3cgjbl4ccu