Fawkes: Protecting Privacy against Unauthorized Deep Learning Models

19 February 2020

Papers citing "Fawkes: Protecting Privacy against Unauthorized Deep Learning Models"

8 / 8 papers shown

Title
Analysis of Adversarial Image Manipulations Ahsi Lo Gabriella Pangelinan Michael C. King PICV 16 0 0 10 May 2023
On the Adversarial Inversion of Deep Biometric Representations Gioacchino Tangari Shreesh Keskar Hassan Jameel Asghar Dali Kaafar AAML 31 2 0 12 Apr 2023
Rethinking Fairness: An Interdisciplinary Survey of Critiques of Hegemonic ML Fairness Approaches Lindsay Weinberg FaML SyDa 29 58 0 06 May 2022
Initiative Defense against Facial Manipulation Qidong Huang Jie Zhang Wenbo Zhou Weiming Zhang Nenghai Yu AAML 21 63 0 19 Dec 2021
Unlearnable Examples: Making Personal Data Unexploitable Hanxun Huang Xingjun Ma S. Erfani James Bailey Yisen Wang MIACV 156 190 0 13 Jan 2021
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses Micah Goldblum Dimitris Tsipras Chulin Xie Xinyun Chen Avi Schwarzschild D. Song A. Madry Bo-wen Li Tom Goldstein SILM 27 270 0 18 Dec 2020
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching Jonas Geiping Liam H. Fowl Yifan Jiang W. Czaja Gavin Taylor Michael Moeller Tom Goldstein AAML 19 215 0 04 Sep 2020
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 287 5,837 0 08 Jul 2016