TEAM: We Need More Powerful Adversarial Examples for DNNs
Authors: Yaguan Qian, Ximin Zhang, Bin Wang, Wei Li, Zhaoquan Gu, Haijiang Wang, Wassim Swaileh | Published: 2020-07-31 | Updated: 2020-08-10
Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases
Authors: Ren Wang, Gaoyuan Zhang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong, Meng Wang | Published: 2020-07-31
LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy
Authors: Lichao Sun, Jianwei Qian, Xun Chen | Published: 2020-07-31 | Updated: 2021-05-21
Membership Leakage in Label-Only Exposures
Authors: Zheng Li, Yang Zhang | Published: 2020-07-30 | Updated: 2021-09-17
Black-box Adversarial Sample Generation Based on Differential Evolution
Authors: Junyu Lin, Lei Xu, Yingqi Liu, Xiangyu Zhang | Published: 2020-07-30
DeepPeep: Exploiting Design Ramifications to Decipher the Architecture of Compact DNNs
Authors: Nandan Kumar Jha, Sparsh Mittal, Binod Kumar, Govardhan Mattela | Published: 2020-07-30
A General Framework For Detecting Anomalous Inputs to DNN Classifiers
Authors: Jayaram Raghuram, Varun Chandrasekaran, Somesh Jha, Suman Banerjee | Published: 2020-07-29 | Updated: 2021-06-17
Dynamic Defense Against Byzantine Poisoning Attacks in Federated Learning
Authors: Nuria Rodríguez-Barroso, Eugenio Martínez-Cámara, M. Victoria Luzón, Francisco Herrera | Published: 2020-07-29 | Updated: 2022-02-24
Adversarial Robustness for Machine Learning Cyber Defenses Using Log Data
Authors: Kai Steverson, Jonathan Mullin, Metin Ahiskali | Published: 2020-07-29
Efficient Sparse Secure Aggregation for Federated Learning
Authors: Constance Beguier, Mathieu Andreux, Eric W. Tramel | Published: 2020-07-29 | Updated: 2021-10-18