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Politics of Adversarial Machine Learning
v1v2v3 (latest)

Politics of Adversarial Machine Learning

Social Science Research Network (SSRN), 2020
1 February 2020
Kendra Albert
J. Penney
B. Schneier
Ramnath Kumar
    AAML
ArXiv (abs)PDFHTML

Papers citing "Politics of Adversarial Machine Learning"

14 / 14 papers shown
Position: Certified Robustness Does Not (Yet) Imply Model Security
Position: Certified Robustness Does Not (Yet) Imply Model Security
Andrew C. Cullen
Paul Montague
S. Erfani
Benjamin I. P. Rubinstein
300
0
0
16 Jun 2025
Online Algorithmic Recourse by Collective Action
Online Algorithmic Recourse by Collective Action
Elliot Creager
Richard Zemel
245
5
0
29 Dec 2023
Adversarial Machine Learning for Social Good: Reframing the Adversary as
  an Ally
Adversarial Machine Learning for Social Good: Reframing the Adversary as an AllyIEEE Transactions on Artificial Intelligence (IEEE TAI), 2023
Shawqi Al-Maliki
Adnan Qayyum
Hassan Ali
M. Abdallah
Junaid Qadir
D. Hoang
Dusit Niyato
Ala I. Al-Fuqaha
AAML
397
7
0
05 Oct 2023
FACE-AUDITOR: Data Auditing in Facial Recognition Systems
FACE-AUDITOR: Data Auditing in Facial Recognition SystemsUSENIX Security Symposium (USENIX Security), 2023
Min Chen
Zhikun Zhang
Tianhao Wang
Michael Backes
Yang Zhang
CVBM
267
25
0
05 Apr 2023
Algorithmic Collective Action in Machine Learning
Algorithmic Collective Action in Machine LearningInternational Conference on Machine Learning (ICML), 2023
Moritz Hardt
Eric Mazumdar
Celestine Mendler-Dünner
Tijana Zrnic
304
33
0
08 Feb 2023
Adversarial Robustness for Tabular Data through Cost and Utility
  Awareness
Adversarial Robustness for Tabular Data through Cost and Utility AwarenessNetwork and Distributed System Security Symposium (NDSS), 2022
Klim Kireev
B. Kulynych
Carmela Troncoso
AAML
428
27
0
27 Aug 2022
Catastrophic overfitting can be induced with discriminative non-robust
  features
Catastrophic overfitting can be induced with discriminative non-robust features
Guillermo Ortiz-Jiménez
Pau de Jorge
Amartya Sanyal
Adel Bibi
P. Dokania
P. Frossard
Grégory Rogez
Juil Sock
AAML
176
3
0
16 Jun 2022
Identifying Adversarial Attacks on Text Classifiers
Identifying Adversarial Attacks on Text Classifiers
Zhouhang Xie
Jonathan Brophy
Adam Noack
Wencong You
Kalyani Asthana
Carter Perkins
Sabrina Reis
Sameer Singh
Daniel Lowd
AAML
186
11
0
21 Jan 2022
Addressing Privacy Threats from Machine Learning
Addressing Privacy Threats from Machine Learning
Mary Anne Smart
144
3
0
25 Oct 2021
The Role of Social Movements, Coalitions, and Workers in Resisting
  Harmful Artificial Intelligence and Contributing to the Development of
  Responsible AI
The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI
Susan von Struensee
152
4
0
11 Jul 2021
Adversarial for Good? How the Adversarial ML Community's Values Impede
  Socially Beneficial Uses of Attacks
Adversarial for Good? How the Adversarial ML Community's Values Impede Socially Beneficial Uses of Attacks
Kendra Albert
Maggie K. Delano
B. Kulynych
Ramnath Kumar
AAML
501
5
0
11 Jul 2021
FoggySight: A Scheme for Facial Lookup Privacy
FoggySight: A Scheme for Facial Lookup PrivacyProceedings on Privacy Enhancing Technologies (PoPETs), 2020
Ivan Evtimov
Pascal Sturmfels
Tadayoshi Kohno
PICVFedML
349
26
0
15 Dec 2020
Pitfalls in Machine Learning Research: Reexamining the Development Cycle
Pitfalls in Machine Learning Research: Reexamining the Development Cycle
Stella Biderman
Walter J. Scheirer
239
26
0
04 Nov 2020
Learning from Positive and Unlabeled Data with Arbitrary Positive Shift
Learning from Positive and Unlabeled Data with Arbitrary Positive ShiftNeural Information Processing Systems (NeurIPS), 2020
Zayd Hammoudeh
Daniel Lowd
541
46
0
24 Feb 2020
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