8 citations · 24 across the 13 of their papers we have counts for
4 papers · 1 filter
Game of Trojans: Adaptive Adversaries Against Output-based Trojaned-Model Detectors
Dinuka Sahabandu, Xiaojun Xu, Arezoo Rajabi +4
We propose and analyze an adaptive adversary that can retrain a Trojaned DNN and is also aware of SOTA output-based Trojaned model detectors. We show that such an adversary can ens…
MDTD: A Multi Domain Trojan Detector for Deep Neural Networks
Arezoo Rajabi, Surudhi Asokraj, Fengqing Jiang +4
Machine learning models that use deep neural networks (DNNs) are vulnerable to backdoor attacks. An adversary carrying out a backdoor attack embeds a predefined perturbation called…
Trojan Horse Training for Breaking Defenses against Backdoor Attacks in Deep Learning
Arezoo Rajabi, Bhaskar Ramasubramanian, Radha Poovendran
Machine learning (ML) models that use deep neural networks are vulnerable to backdoor attacks. Such attacks involve the insertion of a (hidden) trigger by an adversary. As a conseq…
Towards Dependable Deep Convolutional Neural Networks (CNNs) with Out-distribution Learning
Mahdieh Abbasi, Arezoo Rajabi, Christian Gagné +1
Detection and rejection of adversarial examples in security sensitive and safety-critical systems using deep CNNs is essential. In this paper, we propose an approach to augment CNN…