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1607.02533
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Adversarial examples in the physical world
8 July 2016
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
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Papers citing
"Adversarial examples in the physical world"
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Title
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On the Limitation of MagNet Defense against
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Kang-Cheng Chen
Chia-Mu Yu
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114
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Sijia Liu
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Adversarial Attacks and Defences Competition
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Ian Goodfellow
Samy Bengio
Yinpeng Dong
Fangzhou Liao
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Yerkebulan Berdibekov
Takuya Akiba
Seiya Tokui
Motoki Abe
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Defending against Adversarial Images using Basis Functions Transformations
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J. Garritano
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Ethan Weinberger
A. Cloninger
Xiuyuan Cheng
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Y. Kluger
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The Effects of JPEG and JPEG2000 Compression on Attacks using Adversarial Examples
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A. Temi̇zel
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59
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28 Mar 2018
Clipping free attacks against artificial neural networks
B. Addad
Jérôme Kodjabachian
Christophe Meyer
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29
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Detecting Adversarial Perturbations with Saliency
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43
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Understanding Measures of Uncertainty for Adversarial Example Detection
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96
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Sebastián M. Palacio
Jörn Hees
Damian Borth
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Yadong Wang
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80
626
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Yuyin Zhou
Song Bai
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Zhou Ren
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119
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Adversarial Logit Pairing
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Defending against Adversarial Attack towards Deep Neural Networks via Collaborative Multi-task Training
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Chaoran Li
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Surya Nepal
Yang Xiang
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74
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Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples
Zihao Liu
Qi Liu
Tao Liu
Nuo Xu
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Yanzhi Wang
Wujie Wen
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27
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12 Mar 2018
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Stephan Zheng
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48
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On Generation of Adversarial Examples using Convex Programming
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44
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77
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Style Memory: Making a Classifier Network Generative
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Stochastic Activation Pruning for Robust Adversarial Defense
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Kamyar Azizzadenesheli
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96
548
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Jinfeng Yi
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Cho-Jui Hsieh
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116
245
0
03 Mar 2018
Protecting JPEG Images Against Adversarial Attacks
Aaditya (Adi) Prakash
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Solomon Garber
Antonella DiLillo
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80
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Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions
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109
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80
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Retrieval-Augmented Convolutional Neural Networks for Improved Robustness against Adversarial Examples
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160
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Max-Mahalanobis Linear Discriminant Analysis Networks
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83
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Hessian-based Analysis of Large Batch Training and Robustness to Adversaries
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100
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Adversarial Examples that Fool both Computer Vision and Time-Limited Humans
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117
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97
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Adversarial Training for Probabilistic Spiking Neural Networks
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54
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Generalizable Adversarial Examples Detection Based on Bi-model Decision Mismatch
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90
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Out-distribution training confers robustness to deep neural networks
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59
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85
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Are Generative Classifiers More Robust to Adversarial Attacks?
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102
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DARTS: Deceiving Autonomous Cars with Toxic Signs
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117
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Security and Privacy Approaches in Mixed Reality: A Literature Survey
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ASP:A Fast Adversarial Attack Example Generation Framework based on Adversarial Saliency Prediction
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62
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Adversarial Risk and the Dangers of Evaluating Against Weak Attacks
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Aaron van den Oord
Pushmeet Kohli
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182
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Fooling OCR Systems with Adversarial Text Images
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Vitaly Shmatikov
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61
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Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples
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A. Barak
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75
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Certified Robustness to Adversarial Examples with Differential Privacy
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Roxana Geambasu
Daniel J. Hsu
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TSViz: Demystification of Deep Learning Models for Time-Series Analysis
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VISER: Visual Self-Regularization
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A Survey Of Methods For Explaining Black Box Models
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Blind Pre-Processing: A Robust Defense Method Against Adversarial Examples
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