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DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic
  Segmentation
v1v2v3v4 (latest)

DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic Segmentation

IEEE International Joint Conference on Neural Network (IJCNN), 2019
14 August 2019
Yujin Yang
Tae Joon Jun
Byungsoo Oh
Daeyoung Kim
ArXiv (abs)PDFHTML

Papers citing "DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic Segmentation"

15 / 15 papers shown
Detecting Adversarial Attacks in Semantic Segmentation via Uncertainty
  Estimation: A Deep Analysis
Detecting Adversarial Attacks in Semantic Segmentation via Uncertainty Estimation: A Deep Analysis
Kira Maag
Roman Resner
Asja Fischer
AAML
232
0
0
19 Aug 2024
Adversarial Feature Alignment: Balancing Robustness and Accuracy in Deep
  Learning via Adversarial Training
Adversarial Feature Alignment: Balancing Robustness and Accuracy in Deep Learning via Adversarial Training
L. Park
Jaeuk Kim
Myung Gyo Oh
Jaewoo Park
T.-H. Kwon
AAML
418
12
0
19 Feb 2024
Uncertainty-weighted Loss Functions for Improved Adversarial Attacks on
  Semantic Segmentation
Uncertainty-weighted Loss Functions for Improved Adversarial Attacks on Semantic SegmentationIEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2023
Kira Maag
Asja Fischer
AAMLSSeg
225
14
0
26 Oct 2023
Prompt Backdoors in Visual Prompt Learning
Prompt Backdoors in Visual Prompt Learning
Hai Huang
Subrat Kishore Dutta
Michael Backes
Yun Shen
Yang Zhang
VLMVPVLMAAMLSILM
270
3
0
11 Oct 2023
On-Manifold Projected Gradient Descent
On-Manifold Projected Gradient Descent
Aaron Mahler
Tyrus Berry
Thomas Stephens
Harbir Antil
Michael Merritt
Jeanie Schreiber
Ioannis G. Kevrekidis
AAML
290
0
0
23 Aug 2023
Transferable Attack for Semantic Segmentation
Transferable Attack for Semantic Segmentation
Mengqi He
Jing Zhang
Zhaoyuan Yang
Mingyi He
Nick Barnes
Yuchao Dai
268
2
0
31 Jul 2023
Towards Reliable Evaluation and Fast Training of Robust Semantic
  Segmentation Models
Towards Reliable Evaluation and Fast Training of Robust Semantic Segmentation ModelsEuropean Conference on Computer Vision (ECCV), 2023
Francesco Croce
Naman D. Singh
Matthias Hein
VLM
259
13
0
22 Jun 2023
Uncertainty-based Detection of Adversarial Attacks in Semantic
  Segmentation
Uncertainty-based Detection of Adversarial Attacks in Semantic Segmentation
Kira Maag
Asja Fischer
AAMLUQCV
273
5
0
22 May 2023
Dataset Distillation: A Comprehensive Review
Dataset Distillation: A Comprehensive ReviewIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023
Ruonan Yu
Songhua Liu
Xinchao Wang
DD
471
187
0
17 Jan 2023
Backdoor Attacks Against Dataset Distillation
Backdoor Attacks Against Dataset DistillationNetwork and Distributed System Security Symposium (NDSS), 2023
Yugeng Liu
Zheng Li
Michael Backes
Yun Shen
Yang Zhang
DD
281
37
0
03 Jan 2023
Towards Adversarial Purification using Denoising AutoEncoders
Towards Adversarial Purification using Denoising AutoEncoders
Dvij Kalaria
Aritra Hazra
P. Chakrabarti
DiffM
276
8
0
29 Aug 2022
SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and
  Boosting Segmentation Robustness
SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation RobustnessEuropean Conference on Computer Vision (ECCV), 2022
Jindong Gu
Hengshuang Zhao
Volker Tresp
Juil Sock
AAML
325
96
0
25 Jul 2022
A deep learning model for burn depth classification using ultrasound
  imaging
A deep learning model for burn depth classification using ultrasound imagingJournal of The Mechanical Behavior of Biomedical Materials (J Mech Behav Biomed Mater), 2021
Sangrock Lee
Rahul Rahul
James Lukan
Tatiana Boyko
Kateryna Zelenova
Basiel Makled
Conner Parsey
Jack Norfleet
S. De
MedIm
151
17
0
29 Mar 2022
On the Limitations of Denoising Strategies as Adversarial Defenses
On the Limitations of Denoising Strategies as Adversarial Defenses
Zhonghan Niu
Zhaoxi Chen
Linyi Li
Yubin Yang
Yue Liu
Jinfeng Yi
AAML
191
14
0
17 Dec 2020
Investigating Vulnerability to Adversarial Examples on Multimodal Data
  Fusion in Deep Learning
Investigating Vulnerability to Adversarial Examples on Multimodal Data Fusion in Deep Learning
Youngjoon Yu
Hong Joo Lee
Byeong Cheon Kim
Jung Uk Kim
Yong Man Ro
AAML
193
22
0
22 May 2020
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