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The Space of Transferable Adversarial Examples

11 April 2017

Dan Boneh

Papers citing "The Space of Transferable Adversarial Examples"

50 / 302 papers shown

Title
Improved Generalization Bounds for Adversarially Robust Learning Idan Attias A. Kontorovich Yishay Mansour 82 20 0 04 Oct 2018
Adversarial Examples - A Complete Characterisation of the Phenomenon A. Serban E. Poll Joost Visser SILM AAML 102 49 0 02 Oct 2018
Improving the Generalization of Adversarial Training with Domain Adaptation Chuanbiao Song Kun He Liwei Wang John E. Hopcroft AAML OOD 112 132 0 01 Oct 2018
Training Machine Learning Models by Regularizing their Explanations A. Ross FaML 55 0 0 29 Sep 2018
To compress or not to compress: Understanding the Interactions between Adversarial Attacks and Neural Network Compression Yiren Zhao Ilia Shumailov Robert D. Mullins Ross J. Anderson AAML 79 43 0 29 Sep 2018
Neural Networks with Structural Resistance to Adversarial Attacks Luca de Alfaro AAML 45 5 0 25 Sep 2018
Adversarial Binaries for Authorship Identification Xiaozhu Meng B. Miller S. Jha AAML 61 11 0 21 Sep 2018
Adversarial Examples: Opportunities and Challenges Jiliang Zhang Chen Li AAML 57 234 0 13 Sep 2018
Humans can decipher adversarial images Zhenglong Zhou C. Firestone AAML 68 122 0 11 Sep 2018
On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions Yusuke Tsuzuku Issei Sato AAML 82 62 0 11 Sep 2018
Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks Ambra Demontis Marco Melis Maura Pintor Matthew Jagielski Battista Biggio Alina Oprea Cristina Nita-Rotaru Fabio Roli SILM AAML 56 11 0 08 Sep 2018
Are adversarial examples inevitable? Ali Shafahi Wenjie Huang Christoph Studer Soheil Feizi Tom Goldstein SILM 88 283 0 06 Sep 2018
Stochastic Combinatorial Ensembles for Defending Against Adversarial Examples George Adam P. Smirnov David Duvenaud B. Haibe-Kains Anna Goldenberg AAML 39 10 0 20 Aug 2018
Structured Adversarial Attack: Towards General Implementation and Better Interpretability Kaidi Xu Sijia Liu Pu Zhao Pin-Yu Chen Huan Zhang Quanfu Fan Deniz Erdogmus Yanzhi Wang Xinyu Lin AAML 124 162 0 05 Aug 2018
Generalization Error in Deep Learning Daniel Jakubovitz Raja Giryes M. Rodrigues AI4CE 231 111 0 03 Aug 2018
A general metric for identifying adversarial images S. Kumar AAML 26 0 0 26 Jul 2018
Limitations of the Lipschitz constant as a defense against adversarial examples Todd P. Huster C. Chiang R. Chadha AAML 55 84 0 25 Jul 2018
Simultaneous Adversarial Training - Learn from Others Mistakes Zukang Liao AAML GAN 46 4 0 21 Jul 2018
Implicit Generative Modeling of Random Noise during Training for Adversarial Robustness Priyadarshini Panda Kaushik Roy AAML 50 4 0 05 Jul 2018
Adversarial Examples in Deep Learning: Characterization and Divergence Wenqi Wei Ling Liu Margaret Loper Stacey Truex Lei Yu Mehmet Emre Gursoy Yanzhao Wu AAML SILM 119 18 0 29 Jun 2018
Adversarially Robust Training through Structured Gradient Regularization Kevin Roth Aurelien Lucchi Sebastian Nowozin Thomas Hofmann 72 23 0 22 May 2018
Adversarial Attacks on Neural Networks for Graph Data Daniel Zügner Amir Akbarnejad Stephan Günnemann GNN AAML OOD 176 1,075 0 21 May 2018
Overcoming catastrophic forgetting problem by weight consolidation and long-term memory Shixian Wen Laurent Itti CLL 58 16 0 18 May 2018
Breaking Transferability of Adversarial Samples with Randomness Yan Zhou Murat Kantarcioglu B. Xi AAML 49 12 0 11 May 2018
Adversarially Robust Generalization Requires More Data Ludwig Schmidt Shibani Santurkar Dimitris Tsipras Kunal Talwar Aleksander Madry OOD AAML 191 797 0 30 Apr 2018
Towards Dependable Deep Convolutional Neural Networks (CNNs) with Out-distribution Learning Mahdieh Abbasi Arezoo Rajabi Christian Gagné R. Bobba OODD 58 6 0 24 Apr 2018
Adversarial Attacks Against Medical Deep Learning Systems S. G. Finlayson Hyung Won Chung I. Kohane Andrew L. Beam SILM AAML OOD MedIm 76 232 0 15 Apr 2018
Unifying Bilateral Filtering and Adversarial Training for Robust Neural Networks Neale Ratzlaff Fuxin Li AAML FedML 35 1 0 05 Apr 2018
Improving DNN Robustness to Adversarial Attacks using Jacobian Regularization Daniel Jakubovitz Raja Giryes AAML 94 210 0 23 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
Detecting Adversarial Examples - A Lesson from Multimedia Forensics Pascal Schöttle Alexander Schlögl Cecilia Pasquini Rainer Böhme AAML 43 4 0 09 Mar 2018
Stochastic Activation Pruning for Robust Adversarial Defense Guneet Singh Dhillon Kamyar Azizzadenesheli Zachary Chase Lipton Jeremy Bernstein Jean Kossaifi Aran Khanna Anima Anandkumar AAML 94 548 0 05 Mar 2018
Protecting JPEG Images Against Adversarial Attacks Aaditya (Adi) Prakash N. Moran Solomon Garber Antonella DiLillo J. Storer AAML 75 34 0 02 Mar 2018
Understanding and Enhancing the Transferability of Adversarial Examples Lei Wu Zhanxing Zhu Cheng Tai E. Weinan AAML SILM 77 99 0 27 Feb 2018
Predicting Adversarial Examples with High Confidence A. Galloway Graham W. Taylor M. Moussa AAML 56 9 0 13 Feb 2018
Deflecting Adversarial Attacks with Pixel Deflection Aaditya (Adi) Prakash N. Moran Solomon Garber Antonella DiLillo J. Storer AAML 110 304 0 26 Jan 2018
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality Xingjun Ma Yue Liu Yisen Wang S. Erfani S. Wijewickrema Grant Schoenebeck Basel Alomair Michael E. Houle James Bailey AAML 136 742 0 08 Jan 2018
Generating Adversarial Examples with Adversarial Networks Chaowei Xiao Yue Liu Jun-Yan Zhu Warren He M. Liu Basel Alomair GAN AAML 127 904 0 08 Jan 2018
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey Naveed Akhtar Ajmal Mian AAML 144 1,873 0 02 Jan 2018
Exploring the Space of Black-box Attacks on Deep Neural Networks A. Bhagoji Warren He Yue Liu Basel Alomair AAML 28 70 0 27 Dec 2017
The Robust Manifold Defense: Adversarial Training using Generative Models A. Jalal Andrew Ilyas C. Daskalakis A. Dimakis AAML 109 174 0 26 Dec 2017
Adversarial Examples: Attacks and Defenses for Deep Learning Xiaoyong Yuan Pan He Qile Zhu Xiaolin Li SILM AAML 131 1,628 0 19 Dec 2017
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients A. Ross Finale Doshi-Velez AAML 159 688 0 26 Nov 2017
How Wrong Am I? - Studying Adversarial Examples and their Impact on Uncertainty in Gaussian Process Machine Learning Models Kathrin Grosse David Pfaff M. Smith Michael Backes AAML 82 9 0 17 Nov 2017
Attacking Binarized Neural Networks A. Galloway Graham W. Taylor M. Moussa MQ AAML 79 106 0 01 Nov 2017
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples Yang Song Taesup Kim Sebastian Nowozin Stefano Ermon Nate Kushman AAML 137 791 0 30 Oct 2017
Fooling Vision and Language Models Despite Localization and Attention Mechanism Xiaojun Xu Xinyun Chen Chang-rui Liu Anna Rohrbach Trevor Darrell Basel Alomair AAML 99 41 0 25 Sep 2017
Learning Universal Adversarial Perturbations with Generative Models Jamie Hayes G. Danezis AAML 84 54 0 17 Aug 2017
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks John Bradshaw A. G. Matthews Zoubin Ghahramani BDL AAML 117 172 0 08 Jul 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 333 12,166 0 19 Jun 2017