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Adversarial Examples Are Not Bugs, They Are Features

Neural Information Processing Systems (NeurIPS), 2019

6 May 2019

Papers citing "Adversarial Examples Are Not Bugs, They Are Features"

43 / 1,093 papers shown

Interpreting and Improving Adversarial Robustness of Deep Neural Networks with Neuron Sensitivity

Xianglong Liu

348

16 Sep 2019

Towards Model-Agnostic Adversarial Defenses using Adversarially Trained Autoencoders

172

12 Sep 2019

Learning to Disentangle Robust and Vulnerable Features for Adversarial Detection

10 Sep 2019

When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP SignaturesIEEE International Joint Conference on Neural Network (IJCNN), 2019

163

131

08 Sep 2019

Invisible Backdoor Attacks on Deep Neural Networks via Steganography and Regularization

Haojin Zhu

190

06 Sep 2019

Are Adversarial Robustness and Common Perturbation Robustness Independent Attributes ?

172

04 Sep 2019

Universal, transferable and targeted adversarial attacks

Junde Wu

Rao Fu

AAML SILM

152

29 Aug 2019

Adversarial shape perturbations on 3D point clouds

228

16 Aug 2019

Investigating Decision Boundaries of Trained Neural Networks

Roozbeh Yousefzadeh

D. O’Leary

AAML

07 Aug 2019

Random Directional Attack for Fooling Deep Neural Networks

06 Aug 2019

Imperio: Robust Over-the-Air Adversarial Examples for Automatic Speech Recognition SystemsAsia-Pacific Computer Systems Architecture Conference (APCSAC), 2019

381

05 Aug 2019

Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial TrainingNeural Information Processing Systems (NeurIPS), 2019

Haichao Zhang

Jianyu Wang

AAML

373

242

24 Jul 2019

Learning to Find Correlated Features by Maximizing Information Flow in Convolutional Neural Networks

Wei Shen

Fei Li

Rujie Liu

150

30 Jun 2019

Improving performance of deep learning models with axiomatic attribution priors and expected gradients

290

25 Jun 2019

A Fourier Perspective on Model Robustness in Computer VisionNeural Information Processing Systems (NeurIPS), 2019

Dong Yin

Raphael Gontijo-Lopes

Jonathon Shlens

E. D. Cubuk

Justin Gilmer

OOD

406

573

21 Jun 2019

Learning robust visual representations using data augmentation invariance

Alex Hernández-García

Peter König

Tim C Kietzmann

OOD

123

11 Jun 2019

Intriguing properties of adversarial training at scaleInternational Conference on Learning Representations (ICLR), 2019

Cihang Xie

Alan Yuille

AAML

191

10 Jun 2019

A cryptographic approach to black box adversarial machine learning

07 Jun 2019

Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation

Raphael Gontijo-Lopes

Justin Gilmer

308

220

06 Jun 2019

Understanding Adversarial Behavior of DNNs by Disentangling Non-Robust and Robust Components in Performance Metric

Ming-Ming Cheng

113

06 Jun 2019

Adversarial Robustness as a Prior for Learned Representations

230

03 Jun 2019

The Principle of Unchanged Optimality in Reinforcement Learning Generalization

A. Irpan

Xingyou Song

OffRL

136

02 Jun 2019

High Frequency Component Helps Explain the Generalization of Convolutional Neural NetworksComputer Vision and Pattern Recognition (CVPR), 2019

396

623

28 May 2019

Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set RecognitionJournal of Imaging (J. Imaging), 2019

260

28 May 2019

Learning Dynamics of Attention: Human Prior for Interpretable Machine ReasoningNeural Information Processing Systems (NeurIPS), 2019

Wonjae Kim

Yoonho Lee

219

28 May 2019

Robust Classification using Robust Feature Augmentation

198

26 May 2019

Rearchitecting Classification Frameworks For Increased Robustness

293

26 May 2019

Adversarial Distillation for Ordered Top-k Attacks

Zekun Zhang

Tianfu Wu

AAML

126

25 May 2019

Adversarial Policies: Attacking Deep Reinforcement LearningInternational Conference on Learning Representations (ICLR), 2019

Adam Gleave

314

401

25 May 2019

Zero-shot Knowledge Transfer via Adversarial Belief MatchingNeural Information Processing Systems (NeurIPS), 2019

P. Micaelli

Amos Storkey

371

247

23 May 2019

An Empirical Evaluation of Adversarial Robustness under Transfer Learning

263

07 May 2019

Adversarial Training and Robustness for Multiple PerturbationsNeural Information Processing Systems (NeurIPS), 2019

Florian Tramèr

Dan Boneh

AAML SILM

493

411

30 Apr 2019

Cycle-Consistent Adversarial GAN: the integration of adversarial attack and defense

136

12 Apr 2019

Semantics Preserving Adversarial Learning

394

10 Mar 2019

Robust Decision Trees Against Adversarial ExamplesInternational Conference on Machine Learning (ICML), 2019

314

124

27 Feb 2019

Adversarial attacks hidden in plain sight

Barbara Hammer

103

25 Feb 2019

Mockingbird: Defending Against Deep-Learning-Based Website Fingerprinting Attacks with Adversarial Traces

Mohammad Saidur Rahman

Mohsen Imani

Nate Mathews

M. Wright

AAML

316

18 Feb 2019

Increasing the adversarial robustness and explainability of capsule networks with

γ

205

23 Dec 2018

Concise Explanations of Neural Networks using Adversarial Training

551

15 Oct 2018

Large batch size training of neural networks with adversarial training and second-order information

265

02 Oct 2018

Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Convolutional Networks

421

30 Sep 2018

Security and Privacy Issues in Deep Learning

Jaehee Jang

320

31 Jul 2018

DeepFense: Online Accelerated Defense Against Adversarial Deep Learning

229

08 Sep 2017