Systematic Evaluation of Privacy Risks of Machine Learning Models
Authors: Liwei Song, Prateek Mittal | Published: 2020-03-24 | Updated: 2020-12-09
防御手法
Certified Defenses for Adversarial Patches
Authors: Ping-Yeh Chiang, Renkun Ni, Ahmed Abdelkader, Chen Zhu, Christoph Studer, Tom Goldstein | Published: 2020-03-14 | Updated: 2020-09-25
A Survey of Adversarial Learning on Graphs
Authors: Liang Chen, Jintang Li, Jiaying Peng, Tao Xie, Zengxu Cao, Kun Xu, Xiangnan He, Zibin Zheng, Bingzhe Wu | Published: 2020-03-10 | Updated: 2022-04-05
Dynamic Backdoor Attacks Against Machine Learning Models
Authors: Ahmed Salem, Rui Wen, Michael Backes, Shiqing Ma, Yang Zhang | Published: 2020-03-07 | Updated: 2022-03-03
Confusing and Detecting ML Adversarial Attacks with Injected Attractors
Authors: Jiyi Zhang, Ee-Chien Chang, Hwee Kuan Lee | Published: 2020-03-05 | Updated: 2021-03-08
Denoised Smoothing: A Provable Defense for Pretrained Classifiers
Authors: Hadi Salman, Mingjie Sun, Greg Yang, Ashish Kapoor, J. Zico Kolter | Published: 2020-03-04 | Updated: 2020-09-21
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Authors: Francesco Croce, Matthias Hein | Published: 2020-03-03 | Updated: 2020-08-04
Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond
Authors: Kaidi Xu, Zhouxing Shi, Huan Zhang, Yihan Wang, Kai-Wei Chang, Minlie Huang, Bhavya Kailkhura, Xue Lin, Cho-Jui Hsieh | Published: 2020-02-28 | Updated: 2020-10-26
Certified Defense to Image Transformations via Randomized Smoothing
Authors: Marc Fischer, Maximilian Baader, Martin Vechev | Published: 2020-02-27 | Updated: 2021-08-25
Entangled Watermarks as a Defense against Model Extraction
Authors: Hengrui Jia, Christopher A. Choquette-Choo, Varun Chandrasekaran, Nicolas Papernot | Published: 2020-02-27 | Updated: 2021-02-19