Adversarial Risk and the Dangers of Evaluating Against Weak Attacks

15 February 2018

Papers citing "Adversarial Risk and the Dangers of Evaluating Against Weak Attacks"

50 / 151 papers shown

Title
On Adaptive Attacks to Adversarial Example Defenses Florian Tramèr Nicholas Carlini Wieland Brendel A. Madry AAML 104 822 0 19 Feb 2020
Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence S. Raschka Joshua Patterson Corey J. Nolet AI4CE 24 484 0 12 Feb 2020
Fast is better than free: Revisiting adversarial training Eric Wong Leslie Rice J. Zico Kolter AAML OOD 99 1,158 0 12 Jan 2020
Benchmarking Adversarial Robustness Yinpeng Dong Qi-An Fu Xiao Yang Tianyu Pang Hang Su Zihao Xiao Jun Zhu AAML 28 36 0 26 Dec 2019
Enhancing Cross-task Black-Box Transferability of Adversarial Examples with Dispersion Reduction Yantao Lu Yunhan Jia Jianyu Wang Bai Li Weiheng Chai Lawrence Carin Senem Velipasalar AAML 21 81 0 22 Nov 2019
Fine-grained Synthesis of Unrestricted Adversarial Examples Omid Poursaeed Tianxing Jiang Yordanos Goshu Harry Yang Serge J. Belongie Ser-Nam Lim AAML 37 13 0 20 Nov 2019
Adversarial Examples in Modern Machine Learning: A Review R. Wiyatno Anqi Xu Ousmane Amadou Dia A. D. Berker AAML 18 104 0 13 Nov 2019
The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey Olakunle Ibitoye Rana Abou-Khamis Mohamed el Shehaby Ashraf Matrawy M. O. Shafiq AAML 37 68 0 06 Nov 2019
Enhancing Certifiable Robustness via a Deep Model Ensemble Huan Zhang Minhao Cheng Cho-Jui Hsieh 33 9 0 31 Oct 2019
An Alternative Surrogate Loss for PGD-based Adversarial Testing Sven Gowal J. Uesato Chongli Qin Po-Sen Huang Timothy A. Mann Pushmeet Kohli AAML 50 89 0 21 Oct 2019
A New Defense Against Adversarial Images: Turning a Weakness into a Strength Tao Yu Shengyuan Hu Chuan Guo Wei-Lun Chao Kilian Q. Weinberger AAML 58 101 0 16 Oct 2019
Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions He Zhao Trung Le Paul Montague O. Vel Tamas Abraham Dinh Q. Phung AAML 28 8 0 03 Oct 2019
Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks Rémi Bernhard Pierre-Alain Moëllic J. Dutertre AAML MQ 24 18 0 27 Sep 2019
Saccader: Improving Accuracy of Hard Attention Models for Vision Gamaleldin F. Elsayed Simon Kornblith Quoc V. Le VLM 29 72 0 20 Aug 2019
Density estimation in representation space to predict model uncertainty Tiago Ramalho M. Corbalan UQCV BDL 16 38 0 20 Aug 2019
Accurate, reliable and fast robustness evaluation Wieland Brendel Jonas Rauber Matthias Kümmerer Ivan Ustyuzhaninov Matthias Bethge AAML OOD 13 111 0 01 Jul 2019
Evolving Robust Neural Architectures to Defend from Adversarial Attacks Shashank Kotyan Danilo Vasconcellos Vargas OOD AAML 24 36 0 27 Jun 2019
Quantitative Verification of Neural Networks And its Security Applications Teodora Baluta Shiqi Shen Shweta Shinde Kuldeep S. Meel P. Saxena AAML 18 104 0 25 Jun 2019
Robustness Verification of Tree-based Models Hongge Chen Huan Zhang Si Si Yang Li Duane S. Boning Cho-Jui Hsieh AAML 17 76 0 10 Jun 2019
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers Hadi Salman Greg Yang Jungshian Li Pengchuan Zhang Huan Zhang Ilya P. Razenshteyn Sébastien Bubeck AAML 33 536 0 09 Jun 2019
Multi-way Encoding for Robustness Donghyun Kim Sarah Adel Bargal Jianming Zhang Stan Sclaroff AAML 18 2 0 05 Jun 2019
Functional Adversarial Attacks Cassidy Laidlaw S. Feizi AAML 19 183 0 29 May 2019
Certifiably Robust Interpretation in Deep Learning Alexander Levine Sahil Singla S. Feizi FAtt AAML 31 63 0 28 May 2019
ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation Yuzhe Yang Guo Zhang Dina Katabi Zhi Xu AAML 10 168 0 28 May 2019
Scaleable input gradient regularization for adversarial robustness Chris Finlay Adam M. Oberman AAML 16 77 0 27 May 2019
Adversarial Policies: Attacking Deep Reinforcement Learning Adam Gleave Michael Dennis Cody Wild Neel Kant Sergey Levine Stuart J. Russell AAML 27 349 0 25 May 2019
What Do Adversarially Robust Models Look At? Takahiro Itazuri Yoshihiro Fukuhara Hirokatsu Kataoka Shigeo Morishima 19 5 0 19 May 2019
Adversarial Training for Free! Ali Shafahi Mahyar Najibi Amin Ghiasi Zheng Xu John P. Dickerson Christoph Studer L. Davis Gavin Taylor Tom Goldstein AAML 68 1,227 0 29 Apr 2019
Detecting Overfitting via Adversarial Examples Roman Werpachowski András Gyorgy Csaba Szepesvári TDI 26 45 0 06 Mar 2019
A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations Saeid Asgari Taghanaki Kumar Abhishek Shekoofeh Azizi Ghassan Hamarneh AAML 31 40 0 03 Mar 2019
Verification of Non-Linear Specifications for Neural Networks Chongli Qin Krishnamurthy Dvijotham Dvijotham Brendan O'Donoghue Rudy Bunel Robert Stanforth Sven Gowal J. Uesato G. Swirszcz Pushmeet Kohli AAML 14 43 0 25 Feb 2019
Certified Adversarial Robustness via Randomized Smoothing Jeremy M. Cohen Elan Rosenfeld J. Zico Kolter AAML 17 1,995 0 08 Feb 2019
Defending Against Universal Perturbations With Shared Adversarial Training Chaithanya Kumar Mummadi Thomas Brox J. H. Metzen AAML 18 60 0 10 Dec 2018
MMA Training: Direct Input Space Margin Maximization through Adversarial Training G. Ding Yash Sharma Kry Yik-Chau Lui Ruitong Huang AAML 21 270 0 06 Dec 2018
Robustness via curvature regularization, and vice versa Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi J. Uesato P. Frossard AAML 29 318 0 23 Nov 2018
Scalable agent alignment via reward modeling: a research direction Jan Leike David M. Krueger Tom Everitt Miljan Martic Vishal Maini Shane Legg 34 397 0 19 Nov 2018
Theoretical Analysis of Adversarial Learning: A Minimax Approach Zhuozhuo Tu Jingwei Zhang Dacheng Tao AAML 15 68 0 13 Nov 2018
Logit Pairing Methods Can Fool Gradient-Based Attacks Marius Mosbach Maksym Andriushchenko T. A. Trost Matthias Hein Dietrich Klakow AAML 27 82 0 29 Oct 2018
Combinatorial Attacks on Binarized Neural Networks Elias Boutros Khalil Amrita Gupta B. Dilkina AAML 49 40 0 08 Oct 2018
Unrestricted Adversarial Examples Tom B. Brown Nicholas Carlini Chiyuan Zhang Catherine Olsson Paul Christiano Ian Goodfellow AAML 29 101 0 22 Sep 2018
On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions Yusuke Tsuzuku Issei Sato AAML 18 62 0 11 Sep 2018
Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability Kai Y. Xiao Vincent Tjeng Nur Muhammad (Mahi) Shafiullah A. Madry AAML OOD 12 199 0 09 Sep 2018
Monge blunts Bayes: Hardness Results for Adversarial Training Zac Cranko A. Menon Richard Nock Cheng Soon Ong Zhan Shi Christian J. Walder AAML 26 16 0 08 Jun 2018
Re-evaluating Evaluation David Balduzzi K. Tuyls Julien Perolat T. Graepel MoMe 30 97 0 07 Jun 2018
Towards the first adversarially robust neural network model on MNIST Lukas Schott Jonas Rauber Matthias Bethge Wieland Brendel AAML OOD 14 369 0 23 May 2018
Deep Nets: What have they ever done for Vision? Alan Yuille Chenxi Liu 25 100 0 10 May 2018
Adversarial vulnerability for any classifier Alhussein Fawzi Hamza Fawzi Omar Fawzi AAML 33 248 0 23 Feb 2018
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 249 1,842 0 03 Feb 2017
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 296 3,112 0 04 Nov 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 308 5,842 0 08 Jul 2016