Are Odds Really Odd? Bypassing Statistical Detection of Adversarial Examples
Authors: Hossein Hosseini, Sreeram Kannan, Radha Poovendran | Published: 2019-07-28
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Understanding Adversarial Robustness: The Trade-off between Minimum and Average Margin
Authors: Kaiwen Wu, Yaoliang Yu | Published: 2019-07-26
Semisupervised Adversarial Neural Networks for Cyber Security Transfer Learning
Authors: Casey Kneale, Kolia Sadeghi | Published: 2019-07-25
Enhancing Adversarial Example Transferability with an Intermediate Level Attack
Authors: Qian Huang, Isay Katsman, Horace He, Zeqi Gu, Serge Belongie, Ser-Nam Lim | Published: 2019-07-23 | Updated: 2020-02-27
Adversarial Security Attacks and Perturbations on Machine Learning and Deep Learning Methods
Authors: Arif Siddiqi | Published: 2019-07-17
Latent Adversarial Defence with Boundary-guided Generation
Authors: Xiaowei Zhou, Ivor W. Tsang, Jie Yin | Published: 2019-07-16
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning
Authors: Fan Yang, Mengnan Du, Xia Hu | Published: 2019-07-16 | Updated: 2019-08-15
Measuring the Transferability of Adversarial Examples
Authors: Deyan Petrov, Timothy M. Hospedales | Published: 2019-07-14
Adversarial Objects Against LiDAR-Based Autonomous Driving Systems
Authors: Yulong Cao, Chaowei Xiao, Dawei Yang, Jing Fang, Ruigang Yang, Mingyan Liu, Bo Li | Published: 2019-07-11
Why Blocking Targeted Adversarial Perturbations Impairs the Ability to Learn
Authors: Ziv Katzir, Yuval Elovici | Published: 2019-07-11