Downscaling Attack and Defense: Turning What You See Back Into What You Get
Authors: Andrew J. Lohn | Published: 2020-10-06 | Updated: 2020-10-07
Attack Type
Leakage of Dataset Properties in Multi-Party Machine Learning
Authors: Wanrong Zhang, Shruti Tople, Olga Ohrimenko | Published: 2020-06-12 | Updated: 2021-06-17
Robustness to Adversarial Attacks in Learning-Enabled Controllers
Authors: Zikang Xiong, Joe Eappen, He Zhu, Suresh Jagannathan | Published: 2020-06-11
Smoothed Geometry for Robust Attribution
Authors: Zifan Wang, Haofan Wang, Shakul Ramkumar, Matt Fredrikson, Piotr Mardziel, Anupam Datta | Published: 2020-06-11 | Updated: 2020-10-22
Stealing Deep Reinforcement Learning Models for Fun and Profit
Authors: Kangjie Chen, Shangwei Guo, Tianwei Zhang, Xiaofei Xie, Yang Liu | Published: 2020-06-09 | Updated: 2020-12-22
Tricking Adversarial Attacks To Fail
Authors: Blerta Lindqvist | Published: 2020-06-08
On Universalized Adversarial and Invariant Perturbations
Authors: Sandesh Kamath, Amit Deshpande, K V Subrahmanyam | Published: 2020-06-08
mFI-PSO: A Flexible and Effective Method in Adversarial Image Generation for Deep Neural Networks
Authors: Hai Shu, Ronghua Shi, Qiran Jia, Hongtu Zhu, Ziqi Chen | Published: 2020-06-05 | Updated: 2022-05-08
Detecting Audio Attacks on ASR Systems with Dropout Uncertainty
Authors: Tejas Jayashankar, Jonathan Le Roux, Pierre Moulin | Published: 2020-06-02 | Updated: 2020-09-15
Perturbation Analysis of Gradient-based Adversarial Attacks
Authors: Utku Ozbulak, Manvel Gasparyan, Wesley De Neve, Arnout Van Messem | Published: 2020-06-02