Built-in Vulnerabilities to Imperceptible Adversarial Perturbations

19 June 2018

Papers citing "Built-in Vulnerabilities to Imperceptible Adversarial Perturbations"

50 / 54 papers shown

Title
Unrestricted Adversarial Examples Tom B. Brown Nicholas Carlini Chiyuan Zhang Catherine Olsson Paul Christiano Ian Goodfellow AAML 47 103 0 22 Sep 2018
Robustness May Be at Odds with Accuracy Dimitris Tsipras Shibani Santurkar Logan Engstrom Alexander Turner Aleksander Madry AAML 73 1,772 0 30 May 2018
Adversarially Robust Generalization Requires More Data Ludwig Schmidt Shibani Santurkar Dimitris Tsipras Kunal Talwar Aleksander Madry OOD AAML 114 786 0 30 Apr 2018
Adversarial Training Versus Weight Decay A. Galloway T. Tanay Graham W. Taylor AAML 43 23 0 10 Apr 2018
Large Margin Deep Networks for Classification Gamaleldin F. Elsayed Dilip Krishnan H. Mobahi Kevin Regan Samy Bengio MQ 45 282 0 15 Mar 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 159 3,171 0 01 Feb 2018
Audio Adversarial Examples: Targeted Attacks on Speech-to-Text Nicholas Carlini D. Wagner AAML 68 1,076 0 05 Jan 2018
Query-Efficient Black-box Adversarial Examples (superceded) Andrew Ilyas Logan Engstrom Anish Athalye Jessy Lin AAML MLAU 43 53 0 19 Dec 2017
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning Battista Biggio Fabio Roli AAML 85 1,401 0 08 Dec 2017
CycleGAN, a Master of Steganography Casey Chu A. Zhmoginov Mark Sandler GAN 47 212 0 08 Dec 2017
Measuring the tendency of CNNs to Learn Surface Statistical Regularities Jason Jo Yoshua Bengio AAML 57 250 0 30 Nov 2017
Provably Minimally-Distorted Adversarial Examples Nicholas Carlini Guy Katz Clark W. Barrett D. Dill AAML 53 89 0 29 Sep 2017
Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization Luis Muñoz-González Battista Biggio Ambra Demontis Andrea Paudice Vasin Wongrassamee Emil C. Lupu Fabio Roli AAML 89 628 0 29 Aug 2017
BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain Tianyu Gu Brendan Dolan-Gavitt S. Garg SILM 72 1,758 0 22 Aug 2017
Adversarial Examples for Evaluating Reading Comprehension Systems Robin Jia Percy Liang AAML ELM 178 1,594 0 23 Jul 2017
NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles Jiajun Lu Hussein Sibai Evan Fabry David A. Forsyth AAML 57 280 0 12 Jul 2017
Exploring Generalization in Deep Learning Behnam Neyshabur Srinadh Bhojanapalli David A. McAllester Nathan Srebro FAtt 132 1,245 0 27 Jun 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 225 11,962 0 19 Jun 2017
On Calibration of Modern Neural Networks Chuan Guo Geoff Pleiss Yu Sun Kilian Q. Weinberger UQCV 195 5,774 0 14 Jun 2017
Robustness of classifiers to universal perturbations: a geometric perspective Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi Omar Fawzi P. Frossard Stefano Soatto AAML 53 118 0 26 May 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods Nicholas Carlini D. Wagner AAML 103 1,851 0 20 May 2017
Ensemble Adversarial Training: Attacks and Defenses Florian Tramèr Alexey Kurakin Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML 165 2,712 0 19 May 2017
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 134 2,854 0 14 Mar 2017
Adversarial Examples for Semantic Image Segmentation Volker Fischer Mummadi Chaithanya Kumar J. H. Metzen Thomas Brox SSeg GAN AAML 54 119 0 03 Mar 2017
Detecting Adversarial Samples from Artifacts Reuben Feinman Ryan R. Curtin S. Shintre Andrew B. Gardner AAML 74 892 0 01 Mar 2017
Generating Steganographic Images via Adversarial Training Jamie Hayes G. Danezis AAML GAN 47 276 0 01 Mar 2017
Adversarial examples for generative models Jernej Kos Ian S. Fischer D. Song GAN 51 273 0 22 Feb 2017
On the (Statistical) Detection of Adversarial Examples Kathrin Grosse Praveen Manoharan Nicolas Papernot Michael Backes Patrick McDaniel AAML 62 710 0 21 Feb 2017
On Detecting Adversarial Perturbations J. H. Metzen Tim Genewein Volker Fischer Bastian Bischoff AAML 49 947 0 14 Feb 2017
Adversarial Attacks on Neural Network Policies Sandy Huang Nicolas Papernot Ian Goodfellow Yan Duan Pieter Abbeel MLAU AAML 65 832 0 08 Feb 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 290 1,849 0 03 Feb 2017
Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks Vahid Behzadan Arslan Munir AAML SILM 50 275 0 16 Jan 2017
Towards Robust Deep Neural Networks with BANG Andras Rozsa Manuel Günther Terrance E. Boult AAML OOD 36 76 0 01 Dec 2016
Towards the Science of Security and Privacy in Machine Learning Nicolas Papernot Patrick McDaniel Arunesh Sinha Michael P. Wellman AAML 58 472 0 11 Nov 2016
Delving into Transferable Adversarial Examples and Black-box Attacks Yanpei Liu Xinyun Chen Chang-rui Liu D. Song AAML 120 1,727 0 08 Nov 2016
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 448 3,124 0 04 Nov 2016
Universal adversarial perturbations Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi Omar Fawzi P. Frossard AAML 110 2,520 0 26 Oct 2016
Robustness of classifiers: from adversarial to random noise Alhussein Fawzi Seyed-Mohsen Moosavi-Dezfooli P. Frossard AAML 42 371 0 31 Aug 2016
A Boundary Tilting Persepective on the Phenomenon of Adversarial Examples T. Tanay Lewis D. Griffin AAML 54 270 0 27 Aug 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 165 8,513 0 16 Aug 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 489 5,878 0 08 Jul 2016
Measuring Neural Net Robustness with Constraints Osbert Bastani Yani Andrew Ioannou Leonidas Lampropoulos Dimitrios Vytiniotis A. Nori A. Criminisi AAML 50 423 0 24 May 2016
Wide Residual Networks Sergey Zagoruyko N. Komodakis 258 7,951 0 23 May 2016
Practical Black-Box Attacks against Machine Learning Nicolas Papernot Patrick McDaniel Ian Goodfellow S. Jha Z. Berkay Celik A. Swami MLAU AAML 47 3,660 0 08 Feb 2016
Foveation-based Mechanisms Alleviate Adversarial Examples Yan Luo Xavier Boix Gemma Roig T. Poggio Qi Zhao AAML 53 170 0 19 Nov 2015
Adversarial Manipulation of Deep Representations S. Sabour Yanshuai Cao Fartash Faghri David J. Fleet GAN AAML 61 286 0 16 Nov 2015
DeepFool: a simple and accurate method to fool deep neural networks Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi P. Frossard AAML 93 4,878 0 14 Nov 2015
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks Nicolas Papernot Patrick McDaniel Xi Wu S. Jha A. Swami AAML 45 3,061 0 14 Nov 2015
Deep Learning and Music Adversaries Corey Kereliuk Bob L. T. Sturm J. Larsen AAML 42 136 0 16 Jul 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 161 18,922 0 20 Dec 2014