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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 / 1,929 papers shown

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Built-in Vulnerabilities to Imperceptible Adversarial Perturbations T. Tanay Jerone T. A. Andrews Lewis D. Griffin 73 7 0 19 Jun 2018
Non-Negative Networks Against Adversarial Attacks William Fleshman Edward Raff Jared Sylvester Steven Forsyth Mark McLean AAML 66 41 0 15 Jun 2018
Hardware Trojan Attacks on Neural Networks Joseph Clements Yingjie Lao AAML 78 89 0 14 Jun 2018
Manifold Mixup: Better Representations by Interpolating Hidden States Vikas Verma Alex Lamb Christopher Beckham Amir Najafi Ioannis Mitliagkas Aaron Courville David Lopez-Paz Yoshua Bengio AAML DRL 111 35 0 13 Jun 2018
Monge blunts Bayes: Hardness Results for Adversarial Training Zac Cranko A. Menon Richard Nock Cheng Soon Ong Zhan Shi Christian J. Walder AAML 73 17 0 08 Jun 2018
Revisiting Adversarial Risk A. Suggala Adarsh Prasad Vaishnavh Nagarajan Pradeep Ravikumar AAML 60 20 0 07 Jun 2018
Killing four birds with one Gaussian process: the relation between different test-time attacks Kathrin Grosse M. Smith Michael Backes AAML 47 2 0 06 Jun 2018
Resisting Adversarial Attacks using Gaussian Mixture Variational Autoencoders Partha Ghosh Arpan Losalka Michael J. Black AAML 74 78 0 31 May 2018
Scaling provable adversarial defenses Eric Wong Frank R. Schmidt J. H. Metzen J. Zico Kolter AAML 105 450 0 31 May 2018
Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks Kang Liu Brendan Dolan-Gavitt S. Garg AAML 100 1,049 0 30 May 2018
Robustness May Be at Odds with Accuracy Dimitris Tsipras Shibani Santurkar Logan Engstrom Alexander Turner Aleksander Madry AAML 116 1,786 0 30 May 2018
AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networks Chun-Chen Tu Pai-Shun Ting Pin-Yu Chen Sijia Liu Huan Zhang Jinfeng Yi Cho-Jui Hsieh Shin-Ming Cheng MLAU AAML 94 399 0 30 May 2018
Adversarial Examples in Remote Sensing W. Czaja Neil Fendley M. Pekala Christopher R. Ratto I-J. Wang AAML 49 68 0 28 May 2018
GenAttack: Practical Black-box Attacks with Gradient-Free Optimization M. Alzantot Yash Sharma Supriyo Chakraborty Huan Zhang Cho-Jui Hsieh Mani B. Srivastava AAML 103 258 0 28 May 2018
Training verified learners with learned verifiers Krishnamurthy Dvijotham Sven Gowal Robert Stanforth Relja Arandjelović Brendan O'Donoghue J. Uesato Pushmeet Kohli OOD 111 170 0 25 May 2018
Adversarial examples from computational constraints Sébastien Bubeck Eric Price Ilya P. Razenshteyn AAML 151 233 0 25 May 2018
Towards the first adversarially robust neural network model on MNIST Lukas Schott Jonas Rauber Matthias Bethge Wieland Brendel AAML OOD 85 370 0 23 May 2018
Constructing Unrestricted Adversarial Examples with Generative Models Yang Song Rui Shu Nate Kushman Stefano Ermon GAN AAML 218 307 0 21 May 2018
Featurized Bidirectional GAN: Adversarial Defense via Adversarially Learned Semantic Inference Ruying Bao Sihang Liang Qingcan Wang GAN AAML 71 13 0 21 May 2018
Towards Understanding Limitations of Pixel Discretization Against Adversarial Attacks Jiefeng Chen Xi Wu Vaibhav Rastogi Yingyu Liang S. Jha AAML 79 22 0 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 438 37 0 15 May 2018
Detecting Adversarial Samples for Deep Neural Networks through Mutation Testing Jingyi Wang Jun Sun Peixin Zhang Xinyu Wang AAML 76 41 0 14 May 2018
Curriculum Adversarial Training Qi-Zhi Cai Min Du Chang-rui Liu Basel Alomair AAML 91 165 0 13 May 2018
Breaking Transferability of Adversarial Samples with Randomness Yan Zhou Murat Kantarcioglu B. Xi AAML 52 12 0 11 May 2018
Deep Nets: What have they ever done for Vision? Alan Yuille Chenxi Liu 224 104 0 10 May 2018
Verisimilar Percept Sequences Tests for Autonomous Driving Intelligent Agent Assessment Thomio Watanabe D. Wolf 27 8 0 07 May 2018
PRADA: Protecting against DNN Model Stealing Attacks Mika Juuti S. Szyller Samuel Marchal Nadarajah Asokan SILM AAML 107 445 0 07 May 2018
Adversarially Robust Generalization Requires More Data Ludwig Schmidt Shibani Santurkar Dimitris Tsipras Kunal Talwar Aleksander Madry OOD AAML 205 797 0 30 Apr 2018
VectorDefense: Vectorization as a Defense to Adversarial Examples V. Kabilan Brandon L. Morris Anh Totti Nguyen AAML 66 21 0 23 Apr 2018
ADef: an Iterative Algorithm to Construct Adversarial Deformations Rima Alaifari Giovanni S. Alberti Tandri Gauksson AAML 94 97 0 20 Apr 2018
ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector Shang-Tse Chen Cory Cornelius Jason Martin Duen Horng Chau ObjD 213 429 0 16 Apr 2018
Adversarial Attacks Against Medical Deep Learning Systems S. G. Finlayson Hyung Won Chung I. Kohane Andrew L. Beam SILM AAML OOD MedIm 85 232 0 15 Apr 2018
On the Limitation of MagNet Defense against $L_1$ -based Adversarial Examples Pei-Hsuan Lu Pin-Yu Chen Kang-Cheng Chen Chia-Mu Yu AAML 114 19 0 14 Apr 2018
On the Robustness of the CVPR 2018 White-Box Adversarial Example Defenses Anish Athalye Nicholas Carlini AAML 79 170 0 10 Apr 2018
Adversarial Training Versus Weight Decay A. Galloway T. Tanay Graham W. Taylor AAML 70 23 0 10 Apr 2018
An ADMM-Based Universal Framework for Adversarial Attacks on Deep Neural Networks Pu Zhao Sijia Liu Yanzhi Wang Xinyu Lin AAML 72 37 0 09 Apr 2018
Fortified Networks: Improving the Robustness of Deep Networks by Modeling the Manifold of Hidden Representations Alex Lamb Jonathan Binas Anirudh Goyal Dmitriy Serdyuk Sandeep Subramanian Ioannis Mitliagkas Yoshua Bengio OOD 94 43 0 07 Apr 2018
Unifying Bilateral Filtering and Adversarial Training for Robust Neural Networks Neale Ratzlaff Fuxin Li AAML FedML 35 1 0 05 Apr 2018
Defending against Adversarial Images using Basis Functions Transformations Uri Shaham J. Garritano Yutaro Yamada Ethan Weinberger A. Cloninger Xiuyuan Cheng Kelly P. Stanton Y. Kluger AAML 69 57 0 28 Mar 2018
On the Limitation of Local Intrinsic Dimensionality for Characterizing the Subspaces of Adversarial Examples Pei-Hsuan Lu Pin-Yu Chen Chia-Mu Yu AAML 66 26 0 26 Mar 2018
Adversarial Defense based on Structure-to-Signal Autoencoders Joachim Folz Sebastián M. Palacio Jörn Hees Damian Borth Andreas Dengel AAML 71 32 0 21 Mar 2018
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems Lei Ma Felix Juefei Xu Fuyuan Zhang Jiyuan Sun Minhui Xue ... Ting Su Li Li Yang Liu Jianjun Zhao Yadong Wang ELM 80 626 0 20 Mar 2018
A Dual Approach to Scalable Verification of Deep Networks Krishnamurthy Dvijotham Dvijotham Robert Stanforth Sven Gowal Timothy A. Mann Pushmeet Kohli 70 399 0 17 Mar 2018
Adversarial Logit Pairing Harini Kannan Alexey Kurakin Ian Goodfellow AAML 103 629 0 16 Mar 2018
Semantic Adversarial Examples Hossein Hosseini Radha Poovendran GAN AAML 108 199 0 16 Mar 2018
Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples Zihao Liu Qi Liu Tao Liu Nuo Xu Xue Lin Yanzhi Wang Wujie Wen AAML MQ 85 265 0 14 Mar 2018
Invisible Mask: Practical Attacks on Face Recognition with Infrared Zhe Zhou Di Tang Xiaofeng Wang Weili Han Xiangyu Liu Kehuan Zhang CVBM AAML 68 103 0 13 Mar 2018
Detecting Adversarial Examples via Neural Fingerprinting Sumanth Dathathri Stephan Zheng Tianwei Yin Richard M. Murray Yisong Yue MLAU AAML 48 0 0 11 Mar 2018
Style Memory: Making a Classifier Network Generative R. Wiyatno Jeff Orchard 70 4 0 05 Mar 2018
Understanding and Enhancing the Transferability of Adversarial Examples Lei Wu Zhanxing Zhu Cheng Tai E. Weinan AAML SILM 80 99 0 27 Feb 2018