SoK: Unintended Interactions among Machine Learning Defenses and Risks

7 December 2023

Papers citing "SoK: Unintended Interactions among Machine Learning Defenses and Risks"

43 / 93 papers shown

Title
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation Vitaly Feldman Chiyuan Zhang TDI 140 464 0 09 Aug 2020
Label-Only Membership Inference Attacks Christopher A. Choquette-Choo Florian Tramèr Nicholas Carlini Nicolas Papernot MIACV MIALM 90 511 0 28 Jul 2020
Subpopulation Data Poisoning Attacks Matthew Jagielski Giorgio Severi Niklas Pousette Harger Alina Oprea AAML SILM 55 119 0 24 Jun 2020
Domain Generalization using Causal Matching Divyat Mahajan Shruti Tople Amit Sharma OOD 87 335 0 12 Jun 2020
An Overview of Privacy in Machine Learning Emiliano De Cristofaro SILM 59 84 0 18 May 2020
Inverting Gradients -- How easy is it to break privacy in federated learning? Jonas Geiping Hartmut Bauermeister Hannah Dröge Michael Moeller FedML 100 1,228 0 31 Mar 2020
On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping Sanghyun Hong Varun Chandrasekaran Yigitcan Kaya Tudor Dumitras Nicolas Papernot AAML 82 136 0 26 Feb 2020
Rethinking Bias-Variance Trade-off for Generalization of Neural Networks Zitong Yang Yaodong Yu Chong You Jacob Steinhardt Yi-An Ma 67 185 0 26 Feb 2020
Understanding and Mitigating the Tradeoff Between Robustness and Accuracy Aditi Raghunathan Sang Michael Xie Fanny Yang John C. Duchi Percy Liang AAML 84 228 0 25 Feb 2020
Estimating Training Data Influence by Tracing Gradient Descent G. Pruthi Frederick Liu Mukund Sundararajan Satyen Kale TDI 81 408 0 19 Feb 2020
Radioactive data: tracing through training Alexandre Sablayrolles Matthijs Douze Cordelia Schmid Hervé Jégou 74 75 0 03 Feb 2020
Privacy for All: Demystify Vulnerability Disparity of Differential Privacy against Membership Inference Attack Bo Zhang Ruotong Yu Haipei Sun Yanying Li Jun Xu Wendy Hui Wang AAML 45 13 0 24 Jan 2020
Characterizing the Decision Boundary of Deep Neural Networks Hamid Karimi Hanyu Wang Jiliang Tang 46 67 0 24 Dec 2019
Deep Neural Network Fingerprinting by Conferrable Adversarial Examples Nils Lukas Yuxuan Zhang Florian Kerschbaum MLAU FedML AAML 64 145 0 02 Dec 2019
The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks Yuheng Zhang R. Jia Hengzhi Pei Wenxiao Wang Yue Liu D. Song AAML 111 419 0 17 Nov 2019
Thieves on Sesame Street! Model Extraction of BERT-based APIs Kalpesh Krishna Gaurav Singh Tomar Ankur P. Parikh Nicolas Papernot Mohit Iyyer MIACV MLAU 107 201 0 27 Oct 2019
Explanations can be manipulated and geometry is to blame Ann-Kathrin Dombrowski Maximilian Alber Christopher J. Anders M. Ackermann K. Müller Pan Kessel AAML FAtt 81 331 0 19 Jun 2019
Differential Privacy Has Disparate Impact on Model Accuracy Eugene Bagdasaryan Vitaly Shmatikov 142 481 0 28 May 2019
Adversarial Examples Are Not Bugs, They Are Features Andrew Ilyas Shibani Santurkar Dimitris Tsipras Logan Engstrom Brandon Tran Aleksander Madry SILM 89 1,839 0 06 May 2019
HopSkipJumpAttack: A Query-Efficient Decision-Based Attack Jianbo Chen Michael I. Jordan Martin J. Wainwright AAML 63 667 0 03 Apr 2019
Fooling Neural Network Interpretations via Adversarial Model Manipulation Juyeon Heo Sunghwan Joo Taesup Moon AAML FAtt 93 203 0 06 Feb 2019
Reconciling modern machine learning practice and the bias-variance trade-off M. Belkin Daniel J. Hsu Siyuan Ma Soumik Mandal 227 1,647 0 28 Dec 2018
Robustness May Be at Odds with Accuracy Dimitris Tsipras Shibani Santurkar Logan Engstrom Alexander Turner Aleksander Madry AAML 102 1,778 0 30 May 2018
Exploiting Unintended Feature Leakage in Collaborative Learning Luca Melis Congzheng Song Emiliano De Cristofaro Vitaly Shmatikov FedML 145 1,474 0 10 May 2018
A Reductions Approach to Fair Classification Alekh Agarwal A. Beygelzimer Miroslav Dudík John Langford Hanna M. Wallach FaML 224 1,100 0 06 Mar 2018
Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring Yossi Adi Carsten Baum Moustapha Cissé Benny Pinkas Joseph Keshet 61 677 0 13 Feb 2018
Certified Robustness to Adversarial Examples with Differential Privacy Mathias Lécuyer Vaggelis Atlidakis Roxana Geambasu Daniel J. Hsu Suman Jana SILM AAML 94 934 0 09 Feb 2018
A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti A. Monreale Salvatore Ruggieri Franco Turini D. Pedreschi F. Giannotti XAI 124 3,957 0 06 Feb 2018
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) Been Kim Martin Wattenberg Justin Gilmer Carrie J. Cai James Wexler F. Viégas Rory Sayres FAtt 214 1,842 0 30 Nov 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 304 12,063 0 19 Jun 2017
A Closer Look at Memorization in Deep Networks Devansh Arpit Stanislaw Jastrzebski Nicolas Ballas David M. Krueger Emmanuel Bengio ... Tegan Maharaj Asja Fischer Aaron Courville Yoshua Bengio Simon Lacoste-Julien TDI 122 1,818 0 16 Jun 2017
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Kundaje FAtt 198 3,871 0 10 Apr 2017
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 203 2,885 0 14 Mar 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 188 5,986 0 04 Mar 2017
On the (Statistical) Detection of Adversarial Examples Kathrin Grosse Praveen Manoharan Nicolas Papernot Michael Backes Patrick McDaniel AAML 76 713 0 21 Feb 2017
Understanding deep learning requires rethinking generalization Chiyuan Zhang Samy Bengio Moritz Hardt Benjamin Recht Oriol Vinyals HAI 339 4,626 0 10 Nov 2016
Membership Inference Attacks against Machine Learning Models Reza Shokri M. Stronati Congzheng Song Vitaly Shmatikov SLR MIALM MIACV 249 4,122 0 18 Oct 2016
Equality of Opportunity in Supervised Learning Moritz Hardt Eric Price Nathan Srebro FaML 222 4,307 0 07 Oct 2016
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Ramprasaath R. Selvaraju Michael Cogswell Abhishek Das Ramakrishna Vedantam Devi Parikh Dhruv Batra FAtt 297 20,003 0 07 Oct 2016
Stealing Machine Learning Models via Prediction APIs Florian Tramèr Fan Zhang Ari Juels Michael K. Reiter Thomas Ristenpart SILM MLAU 107 1,805 0 09 Sep 2016
Deep Learning with Differential Privacy Martín Abadi Andy Chu Ian Goodfellow H. B. McMahan Ilya Mironov Kunal Talwar Li Zhang FedML SyDa 203 6,121 0 01 Jul 2016
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 277 19,049 0 20 Dec 2014
Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers G. Ateniese G. Felici L. Mancini A. Spognardi Antonio Villani Domenico Vitali 75 460 0 19 Jun 2013