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Adversarial Defense based on Structure-to-Signal Autoencoders

Adversarial Defense based on Structure-to-Signal Autoencoders

21 March 2018
Joachim Folz
Sebastián M. Palacio
Jörn Hees
Damian Borth
Andreas Dengel
    AAML
ArXivPDFHTML

Papers citing "Adversarial Defense based on Structure-to-Signal Autoencoders"

7 / 7 papers shown
Title
Fall Leaf Adversarial Attack on Traffic Sign Classification
Fall Leaf Adversarial Attack on Traffic Sign Classification
Anthony Etim
Jakub Szefer
AAML
71
3
0
27 Nov 2024
Certified Zeroth-order Black-Box Defense with Robust UNet Denoiser
Certified Zeroth-order Black-Box Defense with Robust UNet Denoiser
Astha Verma
A. Subramanyam
Siddhesh Bangar
Naman Lal
R. Shah
Shiníchi Satoh
29
4
0
13 Apr 2023
A Kernelized Manifold Mapping to Diminish the Effect of Adversarial
  Perturbations
A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations
Saeid Asgari Taghanaki
Kumar Abhishek
Shekoofeh Azizi
Ghassan Hamarneh
AAML
31
40
0
03 Mar 2019
An Overview of Computational Approaches for Interpretation Analysis
An Overview of Computational Approaches for Interpretation Analysis
Philipp Blandfort
Jörn Hees
D. Patton
21
2
0
09 Nov 2018
Adversarial Machine Learning at Scale
Adversarial Machine Learning at Scale
Alexey Kurakin
Ian Goodfellow
Samy Bengio
AAML
261
3,109
0
04 Nov 2016
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
281
5,835
0
08 Jul 2016
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust
  Semantic Pixel-Wise Labelling
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling
Vijay Badrinarayanan
Ankur Handa
R. Cipolla
SSeg
166
792
0
27 May 2015
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