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On the Vulnerability of Capsule Networks to Adversarial Attacks

On the Vulnerability of Capsule Networks to Adversarial Attacks

9 June 2019
Félix D. P. Michels
Tobias Uelwer
Eric Upschulte
Stefan Harmeling
    AAML
ArXivPDFHTML

Papers citing "On the Vulnerability of Capsule Networks to Adversarial Attacks"

14 / 14 papers shown
Title
RobCaps: Evaluating the Robustness of Capsule Networks against Affine
  Transformations and Adversarial Attacks
RobCaps: Evaluating the Robustness of Capsule Networks against Affine Transformations and Adversarial Attacks
Alberto Marchisio
Antonio De Marco
Alessio Colucci
Maurizio Martina
Mohamed Bennai
AAML
27
2
0
08 Apr 2023
Why Capsule Neural Networks Do Not Scale: Challenging the Dynamic
  Parse-Tree Assumption
Why Capsule Neural Networks Do Not Scale: Challenging the Dynamic Parse-Tree Assumption
Matthias Mitterreiter
Marcel Koch
Joachim Giesen
Soren Laue
3DPC
MedIm
30
13
0
04 Jan 2023
Recognizing Object by Components with Human Prior Knowledge Enhances
  Adversarial Robustness of Deep Neural Networks
Recognizing Object by Components with Human Prior Knowledge Enhances Adversarial Robustness of Deep Neural Networks
Xiao-Li Li
Ziqi Wang
Bo Zhang
Gang Hua
Xiaolin Hu
32
26
0
04 Dec 2022
Effectiveness of the Recent Advances in Capsule Networks
Effectiveness of the Recent Advances in Capsule Networks
Nidhin Harilal
Rohan Patil
29
0
0
11 Oct 2022
Defensive Distillation based Adversarial Attacks Mitigation Method for
  Channel Estimation using Deep Learning Models in Next-Generation Wireless
  Networks
Defensive Distillation based Adversarial Attacks Mitigation Method for Channel Estimation using Deep Learning Models in Next-Generation Wireless Networks
Ferhat Ozgur Catak
Murat Kuzlu
Evren Çatak
Umit Cali
Ozgur Guler
AAML
25
26
0
12 Aug 2022
Towards Robust Stacked Capsule Autoencoder with Hybrid Adversarial
  Training
Towards Robust Stacked Capsule Autoencoder with Hybrid Adversarial Training
Jiazhu Dai
Siwei Xiong
AAML
31
2
0
28 Feb 2022
The Adversarial Security Mitigations of mmWave Beamforming Prediction
  Models using Defensive Distillation and Adversarial Retraining
The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retraining
Murat Kuzlu
Ferhat Ozgur Catak
Umit Cali
Evren Çatak
Ozgur Guler
AAML
32
9
0
16 Feb 2022
Security Analysis of Capsule Network Inference using Horizontal
  Collaboration
Security Analysis of Capsule Network Inference using Horizontal Collaboration
Adewale Adeyemo
Faiq Khalid
Tolulope A. Odetola
S. R. Hasan
AAML
27
5
0
22 Sep 2021
Capsule Network is Not More Robust than Convolutional Network
Capsule Network is Not More Robust than Convolutional Network
Jindong Gu
Volker Tresp
Han Hu
AAML
6
25
0
29 Mar 2021
Effective and Efficient Vote Attack on Capsule Networks
Effective and Efficient Vote Attack on Capsule Networks
Jindong Gu
Baoyuan Wu
Volker Tresp
AAML
17
26
0
19 Feb 2021
Robust Machine Learning Systems: Challenges, Current Trends,
  Perspectives, and the Road Ahead
Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead
Mohamed Bennai
Mahum Naseer
T. Theocharides
C. Kyrkou
O. Mutlu
Lois Orosa
Jungwook Choi
OOD
81
100
0
04 Jan 2021
An Evasion Attack against Stacked Capsule Autoencoder
An Evasion Attack against Stacked Capsule Autoencoder
Jiazhu Dai
Siwei Xiong
AAML
32
1
0
14 Oct 2020
Detecting and Diagnosing Adversarial Images with Class-Conditional
  Capsule Reconstructions
Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions
Yao Qin
Nicholas Frosst
S. Sabour
Colin Raffel
G. Cottrell
Geoffrey E. Hinton
GAN
AAML
19
71
0
05 Jul 2019
Increasing the adversarial robustness and explainability of capsule
  networks with $γ$-capsules
Increasing the adversarial robustness and explainability of capsule networks with γγγ-capsules
David Peer
Sebastian Stabinger
A. Rodríguez-Sánchez
AAML
GAN
MedIm
39
11
0
23 Dec 2018
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