no code implementations • 21 Jul 2023 • Eldor Abdukhamidov, Mohammed Abuhamad, Simon S. Woo, Eric Chan-Tin, Tamer Abuhmed
Deep learning has been rapidly employed in many applications revolutionizing many industries, but it is known to be vulnerable to adversarial attacks.
no code implementations • 13 Jul 2023 • Eldor Abdukhamidov, Mohammed Abuhamad, Simon S. Woo, Eric Chan-Tin, Tamer Abuhmed
Our results show that the proposed approach is query-efficient with a high attack success rate that can reach between 95% and 100% and transferability with an average success rate of 69% in the ImageNet and CIFAR datasets.
no code implementations • 29 Nov 2022 • Eldor Abdukhamidov, Mohammed Abuhamad, Simon S. Woo, Eric Chan-Tin, Tamer Abuhmed
We assess the effectiveness of proposed attacks against two deep learning model architectures coupled with four interpretation models that represent different categories of interpretation models.
no code implementations • 30 Aug 2018 • Zonghao Huang, Rui Hu, Yuanxiong Guo, Eric Chan-Tin, Yanmin Gong
The goal of this paper is to provide differential privacy for ADMM-based distributed machine learning.