10 citations · 10 across the 5 of their papers we have counts for
8 papers
Adaptive-Gravity: A Defense Against Adversarial Samples
Ali Mirzaeian, Zhi Tian, Sai Manoj P D +4
This paper presents a novel model training solution, denoted as Adaptive-Gravity, for enhancing the robustness of deep neural network classifiers against adversarial examples. We c…
Learning Assisted Side Channel Delay Test for Detection of Recycled ICs
Ashkan Vakil, Farzad Niknia, Ali Mirzaeian +2
With the outsourcing of design flow, ensuring the security and trustworthiness of integrated circuits has become more challenging. Among the security threats, IC counterfeiting and…
Conditional Classification: A Solution for Computational Energy Reduction
Ali Mirzaeian, Sai Manoj, Ashkan Vakil +2
Deep convolutional neural networks have shown high efficiency in computer visions and other applications. However, with the increase in the depth of the networks, the computational…
Diverse Knowledge Distillation (DKD): A Solution for Improving The Robustness of Ensemble Models Against Adversarial Attacks
Ali Mirzaeian, Jana Kosecka, Houman Homayoun +2
This paper proposes an ensemble learning model that is resistant to adversarial attacks. To build resilience, we introduced a training process where each member learns a radically…
LASCA: Learning Assisted Side Channel Delay Analysis for Hardware Trojan Detection
Ashkan Vakil, Farnaz Behnia, Ali Mirzaeian +3
In this paper, we introduce a Learning Assisted Side Channel delay Analysis (LASCA) methodology for Hardware Trojan detection. Our proposed solution, unlike the prior art, does not…
Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks
Farnaz Behnia, Ali Mirzaeian, Mohammad Sabokrou +6
In this paper, we propose Code-Bridged Classifier (CBC), a framework for making a Convolutional Neural Network (CNNs) robust against adversarial attacks without increasing or even…