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Trace and Detect Adversarial Attacks on CNNs using Feature Response Maps

Trace and Detect Adversarial Attacks on CNNs using Feature Response Maps

24 August 2022
Mohammadreza Amirian
Friedhelm Schwenker
Thilo Stadelmann
    AAML
ArXivPDFHTML

Papers citing "Trace and Detect Adversarial Attacks on CNNs using Feature Response Maps"

6 / 6 papers shown
Title
We Can Always Catch You: Detecting Adversarial Patched Objects WITH or
  WITHOUT Signature
We Can Always Catch You: Detecting Adversarial Patched Objects WITH or WITHOUT Signature
Binxiu Liang
Jiachun Li
Jianjun Huang
AAML
17
12
0
09 Jun 2021
Optimizing Information Loss Towards Robust Neural Networks
Optimizing Information Loss Towards Robust Neural Networks
Philip Sperl
Konstantin Böttinger
AAML
11
3
0
07 Aug 2020
Detection of Face Recognition Adversarial Attacks
Detection of Face Recognition Adversarial Attacks
F. V. Massoli
F. Carrara
Giuseppe Amato
Fabrizio Falchi
AAML
14
54
0
05 Dec 2019
Deep Learning in the Wild
Deep Learning in the Wild
Thilo Stadelmann
Mohammadreza Amirian
Ismail Arabaci
M. Arnold
G. Duivesteijn
...
Melanie Geiger
Stefan Lörwald
B. Meier
Katharina Rombach
Lukas Tuggener
11
42
0
13 Jul 2018
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
250
5,833
0
08 Jul 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
249
9,134
0
06 Jun 2015
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