A Little Is Enough: Circumventing Defenses For Distributed Learning
Authors: Moran Baruch, Gilad Baruch, Yoav Goldberg | Published: 2019-02-16
Mitigation of Adversarial Examples in RF Deep Classifiers Utilizing AutoEncoder Pre-training
Authors: Silvija Kokalj-Filipovic, Rob Miller, Nicholas Chang, Chi Leung Lau | Published: 2019-02-16
Adversarial Examples in RF Deep Learning: Detection of the Attack and its Physical Robustness
Authors: Silvija Kokalj-Filipovic, Rob Miller | Published: 2019-02-16
Can Intelligent Hyperparameter Selection Improve Resistance to Adversarial Examples?
Authors: Cody Burkard, Brent Lagesse | Published: 2019-02-14
The Odds are Odd: A Statistical Test for Detecting Adversarial Examples
Authors: Kevin Roth, Yannic Kilcher, Thomas Hofmann | Published: 2019-02-13 | Updated: 2019-05-09
Privacy-Utility Trade-off of Linear Regression under Random Projections and Additive Noise
Authors: Mehrdad Showkatbakhsh, Can Karakus, Suhas Diggavi | Published: 2019-02-13
TensorSCONE: A Secure TensorFlow Framework using Intel SGX
Authors: Roland Kunkel, Do Le Quoc, Franz Gregor, Sergei Arnautov, Pramod Bhatotia, Christof Fetzer | Published: 2019-02-12
Adversarial Samples on Android Malware Detection Systems for IoT Systems
Authors: Xiaolei Liu, Xiaojiang Du, Xiaosong Zhang, Qingxin Zhu, Mohsen Guizani | Published: 2019-02-12
Applications of Machine Learning in Cryptography: A Survey
Authors: Mohammed M. Alani | Published: 2019-02-11
Analyzing, Comparing, and Detecting Emerging Malware: A Graph-based Approach
Authors: Hisham Alasmary, Aminollah Khormali, Afsah Anwar, Jeman Park, Jinchun Choi, DaeHun Nyang, Aziz Mohaisen | Published: 2019-02-11