ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2209.04779
  4. Cited By
Scattering Model Guided Adversarial Examples for SAR Target Recognition:
  Attack and Defense

Scattering Model Guided Adversarial Examples for SAR Target Recognition: Attack and Defense

11 September 2022
Bo Peng
Bo Peng
Jie Zhou
Jianyue Xie
Li Liu
    AAML
ArXiv (abs)PDFHTML

Papers citing "Scattering Model Guided Adversarial Examples for SAR Target Recognition: Attack and Defense"

37 / 37 papers shown
Title
Universal Adversarial Examples in Remote Sensing: Methodology and
  Benchmark
Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark
Yonghao Xu
Pedram Ghamisi
AAML
56
72
0
14 Feb 2022
Attentional Feature Refinement and Alignment Network for Aircraft
  Detection in SAR Imagery
Attentional Feature Refinement and Alignment Network for Aircraft Detection in SAR Imagery
Yan Zhao
Lingjun Zhao
Zhongkang Liu
Dewen Hu
Gangyao Kuang
Li Liu
54
32
0
18 Jan 2022
Universal Spectral Adversarial Attacks for Deformable Shapes
Universal Spectral Adversarial Attacks for Deformable Shapes
Arianna Rampini
Franco Pestarini
Luca Cosmo
Simone Melzi
Emanuele Rodolà
AAML
101
18
0
07 Apr 2021
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a
  Blink
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink
Ranjie Duan
Xiaofeng Mao
•. A. K. Qin
Yun Yang
YueFeng Chen
Shaokai Ye
Yuan He
AAML
43
139
0
11 Mar 2021
Invisible Perturbations: Physical Adversarial Examples Exploiting the
  Rolling Shutter Effect
Invisible Perturbations: Physical Adversarial Examples Exploiting the Rolling Shutter Effect
Athena Sayles
Ashish Hooda
M. Gupta
Rahul Chatterjee
Earlence Fernandes
AAML
51
77
0
26 Nov 2020
Optimism in the Face of Adversity: Understanding and Improving Deep
  Learning through Adversarial Robustness
Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness
Guillermo Ortiz-Jiménez
Apostolos Modas
Seyed-Mohsen Moosavi-Dezfooli
P. Frossard
AAML
93
48
0
19 Oct 2020
Automatic Target Recognition on Synthetic Aperture Radar Imagery: A
  Survey
Automatic Target Recognition on Synthetic Aperture Radar Imagery: A Survey
O. Kechagias-Stamatis
N. Aouf
26
106
0
04 Jul 2020
Sparse-RS: a versatile framework for query-efficient sparse black-box
  adversarial attacks
Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks
Francesco Croce
Maksym Andriushchenko
Naman D. Singh
Nicolas Flammarion
Matthias Hein
83
101
0
23 Jun 2020
Deep Learning Meets SAR
Deep Learning Meets SAR
Xiaoxiang Zhu
S. Montazeri
Mohsin Ali
Yuansheng Hua
Yuanyuan Wang
Lichao Mou
Yilei Shi
Feng Xu
R. Bamler
74
223
0
17 Jun 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke
Sam Gross
Francisco Massa
Adam Lerer
James Bradbury
...
Sasank Chilamkurthy
Benoit Steiner
Lu Fang
Junjie Bai
Soumith Chintala
ODL
520
42,559
0
03 Dec 2019
Sparse and Imperceivable Adversarial Attacks
Sparse and Imperceivable Adversarial Attacks
Francesco Croce
Matthias Hein
AAML
97
199
0
11 Sep 2019
Functional Adversarial Attacks
Functional Adversarial Attacks
Cassidy Laidlaw
Soheil Feizi
AAML
72
185
0
29 May 2019
Adversarial Examples Are Not Bugs, They Are Features
Adversarial Examples Are Not Bugs, They Are Features
Andrew Ilyas
Shibani Santurkar
Dimitris Tsipras
Logan Engstrom
Brandon Tran
Aleksander Madry
SILM
91
1,843
0
06 May 2019
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations
Eric Wong
Frank R. Schmidt
J. Zico Kolter
AAML
78
211
0
21 Feb 2019
Deep Learning for Generic Object Detection: A Survey
Deep Learning for Generic Object Detection: A Survey
Li Liu
Wanli Ouyang
Xiaogang Wang
Paul Fieguth
Jie Chen
Xinwang Liu
M. Pietikäinen
ObjDVLMOOD
162
2,456
0
06 Sep 2018
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture
  Design
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
Ningning Ma
Xiangyu Zhang
Haitao Zheng
Jian Sun
181
5,000
0
30 Jul 2018
Invisible Mask: Practical Attacks on Face Recognition with Infrared
Invisible Mask: Practical Attacks on Face Recognition with Infrared
Zhe Zhou
Di Tang
Xiaofeng Wang
Weili Han
Xiangyu Liu
Kehuan Zhang
CVBMAAML
66
103
0
13 Mar 2018
Texture Classification in Extreme Scale Variations using GANet
Texture Classification in Extreme Scale Variations using GANet
Li Liu
Jie Chen
Guoying Zhao
Paul Fieguth
Xilin Chen
M. Pietikäinen
24
22
0
13 Feb 2018
MobileNetV2: Inverted Residuals and Linear Bottlenecks
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Mark Sandler
Andrew G. Howard
Menglong Zhu
A. Zhmoginov
Liang-Chieh Chen
186
19,316
0
13 Jan 2018
LaVAN: Localized and Visible Adversarial Noise
LaVAN: Localized and Visible Adversarial Noise
D. Karmon
Daniel Zoran
Yoav Goldberg
AAML
75
244
0
08 Jan 2018
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A
  Survey
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
Naveed Akhtar
Ajmal Mian
AAML
97
1,871
0
02 Jan 2018
Countering Adversarial Images using Input Transformations
Countering Adversarial Images using Input Transformations
Chuan Guo
Mayank Rana
Moustapha Cissé
Laurens van der Maaten
AAML
120
1,406
0
31 Oct 2017
Towards Deep Learning Models Resistant to Adversarial Attacks
Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Madry
Aleksandar Makelov
Ludwig Schmidt
Dimitris Tsipras
Adrian Vladu
SILMOOD
310
12,117
0
19 Jun 2017
Feature Squeezing: Detecting Adversarial Examples in Deep Neural
  Networks
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
Weilin Xu
David Evans
Yanjun Qi
AAML
87
1,271
0
04 Apr 2017
A Boundary Tilting Persepective on the Phenomenon of Adversarial
  Examples
A Boundary Tilting Persepective on the Phenomenon of Adversarial Examples
T. Tanay
Lewis D. Griffin
AAML
85
272
0
27 Aug 2016
Densely Connected Convolutional Networks
Densely Connected Convolutional Networks
Gao Huang
Zhuang Liu
Laurens van der Maaten
Kilian Q. Weinberger
PINN3DV
775
36,861
0
25 Aug 2016
Towards Evaluating the Robustness of Neural Networks
Towards Evaluating the Robustness of Neural Networks
Nicholas Carlini
D. Wagner
OODAAML
266
8,579
0
16 Aug 2016
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILMAAML
545
5,909
0
08 Jul 2016
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB
  model size
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
F. Iandola
Song Han
Matthew W. Moskewicz
Khalid Ashraf
W. Dally
Kurt Keutzer
153
7,495
0
24 Feb 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.2K
194,322
0
10 Dec 2015
The Limitations of Deep Learning in Adversarial Settings
The Limitations of Deep Learning in Adversarial Settings
Nicolas Papernot
Patrick McDaniel
S. Jha
Matt Fredrikson
Z. Berkay Celik
A. Swami
AAML
112
3,966
0
24 Nov 2015
DeepFool: a simple and accurate method to fool deep neural networks
DeepFool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli
Alhussein Fawzi
P. Frossard
AAML
151
4,903
0
14 Nov 2015
Striving for Simplicity: The All Convolutional Net
Striving for Simplicity: The All Convolutional Net
Jost Tobias Springenberg
Alexey Dosovitskiy
Thomas Brox
Martin Riedmiller
FAtt
248
4,681
0
21 Dec 2014
Explaining and Harnessing Adversarial Examples
Explaining and Harnessing Adversarial Examples
Ian Goodfellow
Jonathon Shlens
Christian Szegedy
AAMLGAN
280
19,107
0
20 Dec 2014
Deep Neural Networks are Easily Fooled: High Confidence Predictions for
  Unrecognizable Images
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
Anh Totti Nguyen
J. Yosinski
Jeff Clune
AAML
171
3,275
0
05 Dec 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan
Andrew Zisserman
FAttMDE
1.7K
100,479
0
04 Sep 2014
Intriguing properties of neural networks
Intriguing properties of neural networks
Christian Szegedy
Wojciech Zaremba
Ilya Sutskever
Joan Bruna
D. Erhan
Ian Goodfellow
Rob Fergus
AAML
277
14,961
1
21 Dec 2013
1