Adversarial Attacks to Machine Learning-Based Smart Healthcare Systems
Authors: AKM Iqtidar Newaz, Nur Imtiazul Haque, Amit Kumar Sikder, Mohammad Ashiqur Rahman, A. Selcuk Uluagac | Published: 2020-10-07
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples
Authors: Sven Gowal, Chongli Qin, Jonathan Uesato, Timothy Mann, Pushmeet Kohli | Published: 2020-10-07 | Updated: 2021-03-30
CATBERT: Context-Aware Tiny BERT for Detecting Social Engineering Emails
Authors: Younghoo Lee, Joshua Saxe, Richard Harang | Published: 2020-10-07
Not All Datasets Are Born Equal: On Heterogeneous Data and Adversarial Examples
Authors: Yael Mathov, Eden Levy, Ziv Katzir, Asaf Shabtai, Yuval Elovici | Published: 2020-10-07 | Updated: 2021-09-02
Correlated Differential Privacy: Feature Selection in Machine Learning
Authors: Tao Zhang, Tianqing Zhu, Ping Xiong, Huan Huo, Zahir Tari, Wanlei Zhou | Published: 2020-10-07
BAAAN: Backdoor Attacks Against Autoencoder and GAN-Based Machine Learning Models
Authors: Ahmed Salem, Yannick Sautter, Michael Backes, Mathias Humbert, Yang Zhang | Published: 2020-10-06 | Updated: 2020-10-08
InstaHide: Instance-hiding Schemes for Private Distributed Learning
Authors: Yangsibo Huang, Zhao Song, Kai Li, Sanjeev Arora | Published: 2020-10-06 | Updated: 2021-02-24
Constraining Logits by Bounded Function for Adversarial Robustness
Authors: Sekitoshi Kanai, Masanori Yamada, Shin'ya Yamaguchi, Hiroshi Takahashi, Yasutoshi Ida | Published: 2020-10-06
PCAL: A Privacy-preserving Intelligent Credit Risk Modeling Framework Based on Adversarial Learning
Authors: Yuli Zheng, Zhenyu Wu, Ye Yuan, Tianlong Chen, Zhangyang Wang | Published: 2020-10-06
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