FMix: Enhancing Mixed Sample Data Augmentation
Authors: Ethan Harris, Antonia Marcu, Matthew Painter, Mahesan Niranjan, Adam Prügel-Bennett, Jonathon Hare | Published: 2020-02-27 | Updated: 2021-02-28
Revisiting Ensembles in an Adversarial Context: Improving Natural Accuracy
Authors: Aditya Saligrama, Guillaume Leclerc | Published: 2020-02-26
IoT Device Identification Using Deep Learning
Authors: Jaidip Kotak, Yuval Elovici | Published: 2020-02-25
Gödel’s Sentence Is An Adversarial Example But Unsolvable
Authors: Xiaodong Qi, Lansheng Han | Published: 2020-02-25
Towards Backdoor Attacks and Defense in Robust Machine Learning Models
Authors: Ezekiel Soremekun, Sakshi Udeshi, Sudipta Chattopadhyay | Published: 2020-02-25 | Updated: 2023-01-11
Adversarial Perturbations Prevail in the Y-Channel of the YCbCr Color Space
Authors: Camilo Pestana, Naveed Akhtar, Wei Liu, David Glance, Ajmal Mian | Published: 2020-02-25
HYDRA: Pruning Adversarially Robust Neural Networks
Authors: Vikash Sehwag, Shiqi Wang, Prateek Mittal, Suman Jana | Published: 2020-02-24 | Updated: 2020-11-10
Approximate Data Deletion from Machine Learning Models
Authors: Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, James Zou | Published: 2020-02-24 | Updated: 2021-02-23
Stealing Black-Box Functionality Using The Deep Neural Tree Architecture
Authors: Daniel Teitelman, Itay Naeh, Shie Mannor | Published: 2020-02-23
An Accuracy-Lossless Perturbation Method for Defending Privacy Attacks in Federated Learning
Authors: Xue Yang, Yan Feng, Weijun Fang, Jun Shao, Xiaohu Tang, Shu-Tao Xia, Rongxing Lu | Published: 2020-02-23 | Updated: 2021-08-15