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Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples

1 February 2018

Papers citing "Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples"

50 / 1,983 papers shown

Local Gradients Smoothing: Defense against localized adversarial attacksIEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2018

Muzammal Naseer

Salman H. Khan

Fatih Porikli

AAML

265

200

03 Jul 2018

Adversarial Robustness Toolbox v1.0.0

...

423

527

03 Jul 2018

Detection based Defense against Adversarial Examples from the Steganalysis Point of View

264

107

21 Jun 2018

Gradient Adversarial Training of Neural Networks

159

21 Jun 2018

Built-in Vulnerabilities to Imperceptible Adversarial Perturbations

T. Tanay

Jerone T. A. Andrews

Lewis D. Griffin

156

19 Jun 2018

Non-Negative Networks Against Adversarial Attacks

174

15 Jun 2018

Hardware Trojan Attacks on Neural Networks

Joseph Clements

Yingjie Lao

AAML

136

14 Jun 2018

Manifold Mixup: Better Representations by Interpolating Hidden States

Alexia Jolicoeur-Martineau

Aaron Courville

David Lopez-Paz

Yoshua Bengio

AAML DRL

251

13 Jun 2018

Monge blunts Bayes: Hardness Results for Adversarial Training

167

08 Jun 2018

Revisiting Adversarial Risk

203

07 Jun 2018

Killing four birds with one Gaussian process: the relation between different test-time attacks

Kathrin Grosse

M. Smith

Michael Backes

AAML

181

06 Jun 2018

Resisting Adversarial Attacks using Gaussian Mixture Variational Autoencoders

240

31 May 2018

Scaling provable adversarial defenses

J. Zico Kolter

292

464

31 May 2018

Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks

240

1,201

30 May 2018

Robustness May Be at Odds with Accuracy

631

1,884

30 May 2018

AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networks

333

432

30 May 2018

Adversarial Examples in Remote Sensing

28 May 2018

GenAttack: Practical Black-box Attacks with Gradient-Free Optimization

322

279

28 May 2018

Training verified learners with learned verifiers

Krishnamurthy Dvijotham

264

172

25 May 2018

Adversarial examples from computational constraints

406

235

25 May 2018

Towards the first adversarially robust neural network model on MNIST

Wieland Brendel

353

380

23 May 2018

Constructing Unrestricted Adversarial Examples with Generative Models

585

341

21 May 2018

Featurized Bidirectional GAN: Adversarial Defense via Adversarially Learned Semantic Inference

136

21 May 2018

Towards Understanding Limitations of Pixel Discretization Against Adversarial Attacks

280

20 May 2018

Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning

Tribhuvanesh Orekondy

Seong Joon Oh

Yang Zhang

Bernt Schiele

Mario Fritz

PICV FedML

675

15 May 2018

Detecting Adversarial Samples for Deep Neural Networks through Mutation Testing

Jun Sun

192

14 May 2018

Curriculum Adversarial Training

177

182

13 May 2018

Breaking Transferability of Adversarial Samples with Randomness

114

11 May 2018

Deep Nets: What have they ever done for Vision?

Alan Yuille

Chenxi Liu

490

107

10 May 2018

Verisimilar Percept Sequences Tests for Autonomous Driving Intelligent
Agent Assessment

Thomio Watanabe

D. Wolf

07 May 2018

PRADA: Protecting against DNN Model Stealing Attacks

499

488

07 May 2018

Adversarially Robust Generalization Requires More Data

432

839

30 Apr 2018

VectorDefense: Vectorization as a Defense to Adversarial ExamplesStudies in Computational Intelligence (SCI), 2018

145

23 Apr 2018

ADef: an Iterative Algorithm to Construct Adversarial DeformationsInternational Conference on Learning Representations (ICLR), 2018

238

107

20 Apr 2018

ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector

441

457

16 Apr 2018

Adversarial Attacks Against Medical Deep Learning Systems

298

252

15 Apr 2018

On the Limitation of MagNet Defense against

L_1

-based Adversarial Examples

269

14 Apr 2018

On the Robustness of the CVPR 2018 White-Box Adversarial Example Defenses

Anish Athalye

Nicholas Carlini

AAML

161

173

10 Apr 2018

Adversarial Training Versus Weight Decay

229

10 Apr 2018

An ADMM-Based Universal Framework for Adversarial Attacks on Deep Neural Networks

131

09 Apr 2018

Fortified Networks: Improving the Robustness of Deep Networks by Modeling the Manifold of Hidden Representations

Alexia Jolicoeur-Martineau

Yoshua Bengio

OOD

212

07 Apr 2018

Unifying Bilateral Filtering and Adversarial Training for Robust Neural Networks

Neale Ratzlaff

Fuxin Li

AAML FedML

113

05 Apr 2018

Defending against Adversarial Images using Basis Functions Transformations

159

28 Mar 2018

On the Limitation of Local Intrinsic Dimensionality for Characterizing the Subspaces of Adversarial Examples

135

26 Mar 2018

Adversarial Defense based on Structure-to-Signal Autoencoders

141

21 Mar 2018

DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems

...

Ting Su

Yang Liu

344

674

20 Mar 2018

A Dual Approach to Scalable Verification of Deep Networks

Krishnamurthy Dvijotham

249

407

17 Mar 2018

Adversarial Logit Pairing

334

652

16 Mar 2018

Semantic Adversarial Examples

Hossein Hosseini

Radha Poovendran

GAN AAML

306

215

16 Mar 2018

Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial ExamplesComputer Vision and Pattern Recognition (CVPR), 2018

Qi Liu

216

305

14 Mar 2018