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Effective and Robust Detection of Adversarial Examples via
  Benford-Fourier Coefficients

Effective and Robust Detection of Adversarial Examples via Benford-Fourier Coefficients

12 May 2020
Chengcheng Ma
Baoyuan Wu
Shibiao Xu
Yanbo Fan
Yong Zhang
Xiaopeng Zhang
Zhifeng Li
    AAML
ArXivPDFHTML

Papers citing "Effective and Robust Detection of Adversarial Examples via Benford-Fourier Coefficients"

4 / 4 papers shown
Title
Detecting AutoAttack Perturbations in the Frequency Domain
Detecting AutoAttack Perturbations in the Frequency Domain
P. Lorenz
P. Harder
Dominik Strassel
M. Keuper
J. Keuper
AAML
11
13
0
16 Nov 2021
SpectralDefense: Detecting Adversarial Attacks on CNNs in the Fourier
  Domain
SpectralDefense: Detecting Adversarial Attacks on CNNs in the Fourier Domain
P. Harder
Franz-Josef Pfreundt
M. Keuper
J. Keuper
AAML
19
48
0
04 Mar 2021
A New Defense Against Adversarial Images: Turning a Weakness into a
  Strength
A New Defense Against Adversarial Images: Turning a Weakness into a Strength
Tao Yu
Shengyuan Hu
Chuan Guo
Wei-Lun Chao
Kilian Q. Weinberger
AAML
55
101
0
16 Oct 2019
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
287
5,837
0
08 Jul 2016
1