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RAID: Randomized Adversarial-Input Detection for Neural Networks

RAID: Randomized Adversarial-Input Detection for Neural Networks

7 February 2020
Hasan Ferit Eniser
M. Christakis
Valentin Wüstholz
    AAML
ArXivPDFHTML

Papers citing "RAID: Randomized Adversarial-Input Detection for Neural Networks"

5 / 5 papers shown
Title
Resisting Deep Learning Models Against Adversarial Attack
  Transferability via Feature Randomization
Resisting Deep Learning Models Against Adversarial Attack Transferability via Feature Randomization
Ehsan Nowroozi
Mohammadreza Mohammadi
Pargol Golmohammadi
Yassine Mekdad
Mauro Conti
Selcuk Uluagac
AAML
SILM
38
13
0
11 Sep 2022
A Review and Refinement of Surprise Adequacy
A Review and Refinement of Surprise Adequacy
Michael Weiss
Rwiddhi Chakraborty
Paolo Tonella
AAML
AI4TS
16
16
0
10 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
On the Limitation of MagNet Defense against $L_1$-based Adversarial
  Examples
On the Limitation of MagNet Defense against L1L_1L1​-based Adversarial Examples
Pei-Hsuan Lu
Pin-Yu Chen
Kang-Cheng Chen
Chia-Mu Yu
AAML
49
19
0
14 Apr 2018
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
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