"Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice

29 December 2022

Papers citing ""Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice"

43 / 93 papers shown

Title
Adversarial Machine Learning -- Industry Perspectives Ramnath Kumar Magnus Nyström J. Lambert Andrew Marshall Mario Goertzel Andi Comissoneru Matt Swann Sharon Xia AAML SILM 89 235 0 04 Feb 2020
Humpty Dumpty: Controlling Word Meanings via Corpus Poisoning R. Schuster Tal Schuster Yoav Meri Vitaly Shmatikov AAML 43 39 0 14 Jan 2020
Fast is better than free: Revisiting adversarial training Eric Wong Leslie Rice J. Zico Kolter AAML OOD 138 1,179 0 12 Jan 2020
Analyzing Information Leakage of Updates to Natural Language Models Santiago Zanella Béguelin Lukas Wutschitz Shruti Tople Victor Rühle Andrew Paverd O. Ohrimenko Boris Köpf Marc Brockschmidt ELM MIACV FedML PILM KELM 53 125 0 17 Dec 2019
Who is Real Bob? Adversarial Attacks on Speaker Recognition Systems Guangke Chen Sen Chen Lingling Fan Xiaoning Du Zhe Zhao Fu Song Yang Liu AAML 100 196 0 03 Nov 2019
Hear "No Evil", See "Kenansville": Efficient and Transferable Black-Box Attacks on Speech Recognition and Voice Identification Systems H. Abdullah Muhammad Sajidur Rahman Washington Garcia Logan Blue Kevin Warren Anurag Swarnim Yadav T. Shrimpton Patrick Traynor AAML 50 89 0 11 Oct 2019
Detecting AI Trojans Using Meta Neural Analysis Xiaojun Xu Qi Wang Huichen Li Nikita Borisov Carl A. Gunter Yue Liu 84 323 0 08 Oct 2019
MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples Jinyuan Jia Ahmed Salem Michael Backes Yang Zhang Neil Zhenqiang Gong 69 390 0 23 Sep 2019
High Accuracy and High Fidelity Extraction of Neural Networks Matthew Jagielski Nicholas Carlini David Berthelot Alexey Kurakin Nicolas Papernot MLAU MIACV 81 377 0 03 Sep 2019
Federated Learning: Challenges, Methods, and Future Directions Tian Li Anit Kumar Sahu Ameet Talwalkar Virginia Smith FedML 123 4,517 0 21 Aug 2019
Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Fnu Suya Jianfeng Chi David Evans Yuan Tian AAML 62 85 0 19 Aug 2019
Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Di Tang Xiaofeng Wang Haixu Tang Kehuan Zhang AAML 61 201 0 02 Aug 2019
Stolen Memories: Leveraging Model Memorization for Calibrated White-Box Membership Inference Klas Leino Matt Fredrikson MIACV 91 271 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 61 105 0 25 Jun 2019
Terminal Brain Damage: Exposing the Graceless Degradation in Deep Neural Networks Under Hardware Fault Attacks Sanghyun Hong Pietro Frigo Yigitcan Kaya Cristiano Giuffrida Tudor Dumitras AAML 53 211 0 03 Jun 2019
Misleading Authorship Attribution of Source Code using Adversarial Learning Erwin Quiring Alwin Maier Konrad Rieck 46 106 0 29 May 2019
Privacy Risks of Securing Machine Learning Models against Adversarial Examples Liwei Song Reza Shokri Prateek Mittal SILM MIACV AAML 57 241 0 24 May 2019
A critique of the DeepSec Platform for Security Analysis of Deep Learning Models Nicholas Carlini ELM 51 14 0 17 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 132 1,249 0 29 Apr 2019
Gotta Catch Ém All: Using Honeypots to Catch Adversarial Attacks on Neural Networks Shawn Shan Emily Wenger Bolun Wang Yangqiu Song Haitao Zheng Ben Y. Zhao 67 73 0 18 Apr 2019
HopSkipJumpAttack: A Query-Efficient Decision-Based Attack Jianbo Chen Michael I. Jordan Martin J. Wainwright AAML 68 667 0 03 Apr 2019
Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning A. Salem Apratim Bhattacharyya Michael Backes Mario Fritz Yang Zhang FedML AAML MIACV 70 257 0 01 Apr 2019
Attacking Graph-based Classification via Manipulating the Graph Structure Binghui Wang Neil Zhenqiang Gong AAML 87 155 0 01 Mar 2019
On Evaluating Adversarial Robustness Nicholas Carlini Anish Athalye Nicolas Papernot Wieland Brendel Jonas Rauber Dimitris Tsipras Ian Goodfellow Aleksander Madry Alexey Kurakin ELM AAML 89 901 0 18 Feb 2019
CT-GAN: Malicious Tampering of 3D Medical Imagery using Deep Learning Yisroel Mirsky Tom Mahler I. Shelef Yuval Elovici MedIm 57 194 0 11 Jan 2019
TextBugger: Generating Adversarial Text Against Real-world Applications Jinfeng Li S. Ji Tianyu Du Bo Li Ting Wang SILM AAML 211 743 0 13 Dec 2018
Interpretable Deep Learning under Fire Xinyang Zhang Ningfei Wang Hua Shen S. Ji Xiapu Luo Ting Wang AAML AI4CE 102 171 0 03 Dec 2018
AdVersarial: Perceptual Ad Blocking meets Adversarial Machine Learning K. Makarychev Pascal Dupré Yury Makarychev Giancarlo Pellegrino Dan Boneh AAML 98 64 0 08 Nov 2018
Exploring Connections Between Active Learning and Model Extraction Varun Chandrasekaran Kamalika Chaudhuri Irene Giacomelli Shane Walker Songbai Yan MIACV 200 158 0 05 Nov 2018
Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Convolutional Networks Kenneth T. Co Luis Muñoz-González Sixte de Maupeou Emil C. Lupu AAML 61 67 0 30 Sep 2018
Motivating the Rules of the Game for Adversarial Example Research Justin Gilmer Ryan P. Adams Ian Goodfellow David G. Andersen George E. Dahl AAML 85 229 0 18 Jul 2018
Adversarial Perturbations Against Real-Time Video Classification Systems Shasha Li Ajaya Neupane S. Paul Chengyu Song S. Krishnamurthy Amit K. Roy-Chowdhury A. Swami AAML 81 119 0 02 Jul 2018
ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models A. Salem Yang Zhang Mathias Humbert Pascal Berrang Mario Fritz Michael Backes MIACV MIALM 96 949 0 04 Jun 2018
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning Battista Biggio Fabio Roli AAML 130 1,409 0 08 Dec 2017
Improving Robustness of ML Classifiers against Realizable Evasion Attacks Using Conserved Features Liang Tong Yue Liu Chen Hajaj Chaowei Xiao Ning Zhang Yevgeniy Vorobeychik AAML OOD 36 87 0 28 Aug 2017
Evasion Attacks against Machine Learning at Test Time Battista Biggio Igino Corona Davide Maiorca B. Nelson Nedim Srndic Pavel Laskov Giorgio Giacinto Fabio Roli AAML 157 2,153 0 21 Aug 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 310 12,069 0 19 Jun 2017
Membership Inference Attacks against Machine Learning Models Reza Shokri M. Stronati Congzheng Song Vitaly Shmatikov SLR MIALM MIACV 261 4,135 0 18 Oct 2016
Stealing Machine Learning Models via Prediction APIs Florian Tramèr Fan Zhang Ari Juels Michael K. Reiter Thomas Ristenpart SILM MLAU 107 1,807 0 09 Sep 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 266 8,555 0 16 Aug 2016
Practical Black-Box Attacks against Machine Learning Nicolas Papernot Patrick McDaniel Ian Goodfellow S. Jha Z. Berkay Celik A. Swami MLAU AAML 75 3,678 0 08 Feb 2016
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 277 14,927 1 21 Dec 2013
Poisoning Attacks against Support Vector Machines Battista Biggio B. Nelson Pavel Laskov AAML 115 1,593 0 27 Jun 2012