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Physical Adversarial Attacks on Deep Neural Networks for Traffic Sign
  Recognition: A Feasibility Study

Physical Adversarial Attacks on Deep Neural Networks for Traffic Sign Recognition: A Feasibility Study

27 February 2023
Fabian Woitschek
G. Schneider
    AAML
ArXivPDFHTML

Papers citing "Physical Adversarial Attacks on Deep Neural Networks for Traffic Sign Recognition: A Feasibility Study"

6 / 6 papers shown
Title
Evaluating Adversarial Attacks on Traffic Sign Classifiers beyond
  Standard Baselines
Evaluating Adversarial Attacks on Traffic Sign Classifiers beyond Standard Baselines
Svetlana Pavlitska
Leopold Müller
J. Marius Zöllner
AAML
76
0
0
12 Dec 2024
Fall Leaf Adversarial Attack on Traffic Sign Classification
Fall Leaf Adversarial Attack on Traffic Sign Classification
Anthony Etim
Jakub Szefer
AAML
76
3
0
27 Nov 2024
Diffusion Attack: Leveraging Stable Diffusion for Naturalistic Image
  Attacking
Diffusion Attack: Leveraging Stable Diffusion for Naturalistic Image Attacking
Qianyu Guo
Jiaming Fu
Yawen Lu
Dongming Gan
DiffM
27
0
0
21 Mar 2024
Adversarial Attacks on Traffic Sign Recognition: A Survey
Adversarial Attacks on Traffic Sign Recognition: A Survey
Svetlana Pavlitska
Nico Lambing
J. Marius Zöllner
AAML
27
17
0
17 Jul 2023
Online Black-Box Confidence Estimation of Deep Neural Networks
Online Black-Box Confidence Estimation of Deep Neural Networks
Fabian Woitschek
G. Schneider
UQCV
23
1
0
27 Feb 2023
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