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Spatially Transformed Adversarial Examples
v1v2 (latest)

Spatially Transformed Adversarial Examples

8 January 2018
Chaowei Xiao
Jun-Yan Zhu
Yue Liu
Warren He
M. Liu
Basel Alomair
    AAML
ArXiv (abs)PDFHTML

Papers citing "Spatially Transformed Adversarial Examples"

50 / 219 papers shown
Title
Adversarial Sensor Attack on LiDAR-based Perception in Autonomous
  Driving
Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving
Yulong Cao
Chaowei Xiao
Benjamin Cyr
Yimeng Zhou
Wonseok Park
Sara Rampazzi
Qi Alfred Chen
Kevin Fu
Z. Morley Mao
AAML
63
544
0
16 Jul 2019
Adversarial Objects Against LiDAR-Based Autonomous Driving Systems
Adversarial Objects Against LiDAR-Based Autonomous Driving Systems
Yulong Cao
Chaowei Xiao
Dawei Yang
Jin Fang
Ruigang Yang
Mingyan D. Liu
Yue Liu
3DPCAAML
86
142
0
11 Jul 2019
Cloud-based Image Classification Service Is Not Robust To Simple
  Transformations: A Forgotten Battlefield
Cloud-based Image Classification Service Is Not Robust To Simple Transformations: A Forgotten Battlefield
Dou Goodman
Tao Wei
AAML
69
6
0
19 Jun 2019
SemanticAdv: Generating Adversarial Examples via Attribute-conditional
  Image Editing
SemanticAdv: Generating Adversarial Examples via Attribute-conditional Image Editing
Haonan Qiu
Chaowei Xiao
Lei Yang
Xinchen Yan
Honglak Lee
Yue Liu
AAML
69
172
0
19 Jun 2019
The Attack Generator: A Systematic Approach Towards Constructing
  Adversarial Attacks
The Attack Generator: A Systematic Approach Towards Constructing Adversarial Attacks
F. Assion
Peter Schlicht
Florens Greßner
W. Günther
Fabian Hüger
Nico M. Schmidt
Umair Rasheed
AAML
75
14
0
17 Jun 2019
Towards Stable and Efficient Training of Verifiably Robust Neural
  Networks
Towards Stable and Efficient Training of Verifiably Robust Neural Networks
Huan Zhang
Hongge Chen
Chaowei Xiao
Sven Gowal
Robert Stanforth
Yue Liu
Duane S. Boning
Cho-Jui Hsieh
AAML
109
351
0
14 Jun 2019
Adversarial Attack Generation Empowered by Min-Max Optimization
Adversarial Attack Generation Empowered by Min-Max Optimization
Jingkang Wang
Tianyun Zhang
Sijia Liu
Pin-Yu Chen
Jiacen Xu
M. Fardad
Yangqiu Song
AAML
83
39
0
09 Jun 2019
Efficient Project Gradient Descent for Ensemble Adversarial Attack
Efficient Project Gradient Descent for Ensemble Adversarial Attack
Fanyou Wu
R. Gazo
E. Haviarova
Bedrich Benes
AAML
33
5
0
07 Jun 2019
Functional Adversarial Attacks
Functional Adversarial Attacks
Cassidy Laidlaw
Soheil Feizi
AAML
100
185
0
29 May 2019
Interpreting Adversarially Trained Convolutional Neural Networks
Interpreting Adversarially Trained Convolutional Neural Networks
Tianyuan Zhang
Zhanxing Zhu
AAMLGANFAtt
125
161
0
23 May 2019
Taking Care of The Discretization Problem: A Comprehensive Study of the
  Discretization Problem and A Black-Box Adversarial Attack in Discrete Integer
  Domain
Taking Care of The Discretization Problem: A Comprehensive Study of the Discretization Problem and A Black-Box Adversarial Attack in Discrete Integer Domain
Lei Bu
Yuchao Duan
Fu Song
Zhe Zhao
AAML
114
18
0
19 May 2019
Harnessing the Vulnerability of Latent Layers in Adversarially Trained
  Models
Harnessing the Vulnerability of Latent Layers in Adversarially Trained Models
M. Singh
Abhishek Sinha
Nupur Kumari
Harshitha Machiraju
Balaji Krishnamurthy
V. Balasubramanian
AAML
56
61
0
13 May 2019
ROSA: Robust Salient Object Detection against Adversarial Attacks
ROSA: Robust Salient Object Detection against Adversarial Attacks
Haofeng Li
Guanbin Li
Yizhou Yu
AAML
75
29
0
09 May 2019
Transfer of Adversarial Robustness Between Perturbation Types
Transfer of Adversarial Robustness Between Perturbation Types
Daniel Kang
Yi Sun
Tom B. Brown
Dan Hendrycks
Jacob Steinhardt
AAML
71
49
0
03 May 2019
Making Convolutional Networks Shift-Invariant Again
Making Convolutional Networks Shift-Invariant Again
Richard Y. Zhang
OOD
105
799
0
25 Apr 2019
Semantic Adversarial Attacks: Parametric Transformations That Fool Deep
  Classifiers
Semantic Adversarial Attacks: Parametric Transformations That Fool Deep Classifiers
Ameya Joshi
Amitangshu Mukherjee
Soumik Sarkar
Chinmay Hegde
AAML
92
100
0
17 Apr 2019
Unrestricted Adversarial Examples via Semantic Manipulation
Unrestricted Adversarial Examples via Semantic Manipulation
Anand Bhattad
Min Jin Chong
Kaizhao Liang
Yangqiu Song
David A. Forsyth
AAML
85
153
0
12 Apr 2019
Learning to Generate Synthetic Data via Compositing
Learning to Generate Synthetic Data via Compositing
Shashank Tripathi
Siddhartha Chandra
Amit Agrawal
A. Tyagi
James M. Rehg
Visesh Chari
105
120
0
10 Apr 2019
Interpreting Adversarial Examples by Activation Promotion and
  Suppression
Interpreting Adversarial Examples by Activation Promotion and Suppression
Kaidi Xu
Sijia Liu
Gaoyuan Zhang
Mengshu Sun
Pu Zhao
Quanfu Fan
Chuang Gan
Xinyu Lin
AAMLFAtt
147
43
0
03 Apr 2019
SpaceNet MVOI: a Multi-View Overhead Imagery Dataset
SpaceNet MVOI: a Multi-View Overhead Imagery Dataset
N. Weir
David Lindenbaum
A. Bastidas
A. V. Etten
Sean McPherson
Jacob Shermeyer
V. Vijay
Hanlin Tang
83
71
0
28 Mar 2019
Rallying Adversarial Techniques against Deep Learning for Network
  Security
Rallying Adversarial Techniques against Deep Learning for Network Security
Joseph Clements
Yuzhe Yang
Ankur A Sharma
Hongxin Hu
Yingjie Lao
AAML
80
52
0
27 Mar 2019
Quantifying Perceptual Distortion of Adversarial Examples
Quantifying Perceptual Distortion of Adversarial Examples
Matt Jordan
N. Manoj
Surbhi Goel
A. Dimakis
68
39
0
21 Feb 2019
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations
Eric Wong
Frank R. Schmidt
J. Zico Kolter
AAML
95
211
0
21 Feb 2019
advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch
advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch
G. Ding
Luyu Wang
Xiaomeng Jin
79
183
0
20 Feb 2019
Do ImageNet Classifiers Generalize to ImageNet?
Do ImageNet Classifiers Generalize to ImageNet?
Benjamin Recht
Rebecca Roelofs
Ludwig Schmidt
Vaishaal Shankar
OODSSegVLM
138
1,733
0
13 Feb 2019
Adversarial Examples Are a Natural Consequence of Test Error in Noise
Adversarial Examples Are a Natural Consequence of Test Error in Noise
Nic Ford
Justin Gilmer
Nicholas Carlini
E. D. Cubuk
AAML
140
320
0
29 Jan 2019
Theoretically Principled Trade-off between Robustness and Accuracy
Theoretically Principled Trade-off between Robustness and Accuracy
Hongyang R. Zhang
Yaodong Yu
Jiantao Jiao
Eric Xing
L. Ghaoui
Michael I. Jordan
259
2,566
0
24 Jan 2019
Adversarial Attack and Defense on Graph Data: A Survey
Adversarial Attack and Defense on Graph Data: A Survey
Lichao Sun
Yingtong Dou
Carl Yang
Ji Wang
Yixin Liu
Philip S. Yu
Lifang He
Yangqiu Song
GNNAAML
139
286
0
26 Dec 2018
A Survey of Safety and Trustworthiness of Deep Neural Networks:
  Verification, Testing, Adversarial Attack and Defence, and Interpretability
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability
Xiaowei Huang
Daniel Kroening
Wenjie Ruan
Marta Kwiatkowska
Youcheng Sun
Emese Thamo
Min Wu
Xinping Yi
AAML
132
51
0
18 Dec 2018
Adversarial Sample Detection for Deep Neural Network through Model
  Mutation Testing
Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing
Jingyi Wang
Guoliang Dong
Jun Sun
Xinyu Wang
Peixin Zhang
AAML
80
191
0
14 Dec 2018
Interpretable Deep Learning under Fire
Interpretable Deep Learning under Fire
Xinyang Zhang
Ningfei Wang
Hua Shen
S. Ji
Xiapu Luo
Ting Wang
AAMLAI4CE
138
174
0
03 Dec 2018
Disentangling Adversarial Robustness and Generalization
Disentangling Adversarial Robustness and Generalization
David Stutz
Matthias Hein
Bernt Schiele
AAMLOOD
313
285
0
03 Dec 2018
Attacks on State-of-the-Art Face Recognition using Attentional
  Adversarial Attack Generative Network
Attacks on State-of-the-Art Face Recognition using Attentional Adversarial Attack Generative Network
Q. Song
Yingqi Wu
Lu Yang
AAMLCVBMGAN
125
98
0
29 Nov 2018
Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses
  of Familiar Objects
Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects
Michael A. Alcorn
Melvin Johnson
Zhitao Gong
Chengfei Wang
Long Mai
Naveen Ari
Stella Laurenzo
115
299
0
28 Nov 2018
Convolutional Neural Networks with Transformed Input based on Robust
  Tensor Network Decomposition
Convolutional Neural Networks with Transformed Input based on Robust Tensor Network Decomposition
Jenn-Bing Ong
W. Ng
C.-C. Jay Kuo
AAML
55
0
0
20 Nov 2018
AdVersarial: Perceptual Ad Blocking meets Adversarial Machine Learning
AdVersarial: Perceptual Ad Blocking meets Adversarial Machine Learning
K. Makarychev
Pascal Dupré
Yury Makarychev
Giancarlo Pellegrino
Dan Boneh
AAML
104
64
0
08 Nov 2018
SparseFool: a few pixels make a big difference
SparseFool: a few pixels make a big difference
Apostolos Modas
Seyed-Mohsen Moosavi-Dezfooli
P. Frossard
AAML
72
200
0
06 Nov 2018
Data Poisoning Attack against Unsupervised Node Embedding Methods
Data Poisoning Attack against Unsupervised Node Embedding Methods
Mingjie Sun
Jian Tang
Huichen Li
Yue Liu
Chaowei Xiao
Yao-Liang Chen
Basel Alomair
GNNAAML
50
67
0
30 Oct 2018
MeshAdv: Adversarial Meshes for Visual Recognition
MeshAdv: Adversarial Meshes for Visual Recognition
Chaowei Xiao
Dawei Yang
Yue Liu
Jia Deng
M. Liu
AAML
63
25
0
11 Oct 2018
Characterizing Adversarial Examples Based on Spatial Consistency
  Information for Semantic Segmentation
Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation
Chaowei Xiao
Ruizhi Deng
Yue Liu
Feng Yu
M. Liu
Basel Alomair
AAML
59
99
0
11 Oct 2018
Adversarial Examples - A Complete Characterisation of the Phenomenon
Adversarial Examples - A Complete Characterisation of the Phenomenon
A. Serban
E. Poll
Joost Visser
SILMAAML
102
49
0
02 Oct 2018
Procedural Noise Adversarial Examples for Black-Box Attacks on Deep
  Convolutional Networks
Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Convolutional Networks
Kenneth T. Co
Luis Muñoz-González
Sixte de Maupeou
Emil C. Lupu
AAML
74
67
0
30 Sep 2018
Fast Geometrically-Perturbed Adversarial Faces
Fast Geometrically-Perturbed Adversarial Faces
Ali Dabouei
Sobhan Soleymani
J. Dawson
Nasser M. Nasrabadi
CVBMAAML
64
65
0
24 Sep 2018
Generating 3D Adversarial Point Clouds
Generating 3D Adversarial Point Clouds
Chong Xiang
C. Qi
Yue Liu
3DPC
117
294
0
19 Sep 2018
DeepHunter: Hunting Deep Neural Network Defects via Coverage-Guided
  Fuzzing
DeepHunter: Hunting Deep Neural Network Defects via Coverage-Guided Fuzzing
Xiaofei Xie
Lei Ma
Felix Juefei Xu
Hongxu Chen
Minhui Xue
Yue Liu
Yang Liu
Jianjun Zhao
Jianxiong Yin
Simon See
116
41
0
04 Sep 2018
Defense Against Adversarial Attacks with Saak Transform
Defense Against Adversarial Attacks with Saak Transform
Sibo Song
Yueru Chen
Ngai-Man Cheung
C.-C. Jay Kuo
69
24
0
06 Aug 2018
Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the
  Robustness of 18 Deep Image Classification Models
Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models
D. Su
Huan Zhang
Hongge Chen
Jinfeng Yi
Pin-Yu Chen
Yupeng Gao
VLM
140
393
0
05 Aug 2018
Structured Adversarial Attack: Towards General Implementation and Better
  Interpretability
Structured Adversarial Attack: Towards General Implementation and Better Interpretability
Kaidi Xu
Sijia Liu
Pu Zhao
Pin-Yu Chen
Huan Zhang
Quanfu Fan
Deniz Erdogmus
Yanzhi Wang
Xinyu Lin
AAML
126
162
0
05 Aug 2018
Attack and defence in cellular decision-making: lessons from machine
  learning
Attack and defence in cellular decision-making: lessons from machine learning
Thomas J. Rademaker
Emmanuel Bengio
P. Franccois
AAML
51
4
0
10 Jul 2018
DeepLaser: Practical Fault Attack on Deep Neural Networks
DeepLaser: Practical Fault Attack on Deep Neural Networks
J. Breier
Xiaolu Hou
Dirmanto Jap
Lei Ma
S. Bhasin
Yang Liu
AAMLAI4CE
80
19
0
15 Jun 2018
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