DNNShield: Dynamic Randomized Model Sparsification, A Defense Against Adversarial Machine Learning
Authors: Mohammad Hossein Samavatian, Saikat Majumdar, Kristin Barber, Radu Teodorescu | Published: 2022-07-31
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Fine-grained Private Knowledge Distillation
Authors: Yuntong Li, Shaowei Wang, Yingying Wang, Jin Li, Yuqiu Qian, Bangzhou Xin, Wei Yang | Published: 2022-07-27 | Updated: 2023-04-06
Careful What You Wish For: on the Extraction of Adversarially Trained Models
Authors: Kacem Khaled, Gabriela Nicolescu, Felipe Gohring de Magalhães | Published: 2022-07-21
One-vs-the-Rest Loss to Focus on Important Samples in Adversarial Training
Authors: Sekitoshi Kanai, Shin'ya Yamaguchi, Masanori Yamada, Hiroshi Takahashi, Kentaro Ohno, Yasutoshi Ida | Published: 2022-07-21 | Updated: 2023-04-26
How to Steer Your Adversary: Targeted and Efficient Model Stealing Defenses with Gradient Redirection
Authors: Mantas Mazeika, Bo Li, David Forsyth | Published: 2022-06-28
Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective
Authors: Mark Huasong Meng, Guangdong Bai, Sin Gee Teo, Zhe Hou, Yan Xiao, Yun Lin, Jin Song Dong | Published: 2022-06-24 | Updated: 2022-10-11
The Role of Machine Learning in Cybersecurity
Authors: Giovanni Apruzzese, Pavel Laskov, Edgardo Montes de Oca, Wissam Mallouli, Luis Burdalo Rapa, Athanasios Vasileios Grammatopoulos, Fabio Di Franco | Published: 2022-06-20
On the Role of Generalization in Transferability of Adversarial Examples
Authors: Yilin Wang, Farzan Farnia | Published: 2022-06-18
Adversarial Robustness is at Odds with Lazy Training
Authors: Yunjuan Wang, Enayat Ullah, Poorya Mianjy, Raman Arora | Published: 2022-06-18 | Updated: 2022-10-17
Morphence-2.0: Evasion-Resilient Moving Target Defense Powered by Out-of-Distribution Detection
Authors: Abderrahmen Amich, Ata Kaboudi, Birhanu Eshete | Published: 2022-06-15