Advancing Security in AI Systems: A Novel Approach to Detecting Backdoors in Deep Neural Networks

arXiv:2403.08208v1 Announce Type: cross
Abstract: In the rapidly evolving landscape of communication and network security, the increasing reliance on deep neural networks (DNNs) and cloud services for data processing presents a significant vulnerability: the potential for backdoors that can be exploited by malicious actors. Our approach leverages advanced tensor decomposition algorithms Independent Vector Analysis (IVA), Multiset Canonical Correlation Analysis (MCCA), and Parallel Factor Analysis (PARAFAC2) to meticulously analyze the weights of pre-trained DNNs and distinguish between backdoored and clean models …

abstract actors ai systems arxiv cloud cloud services communication cs.cr cs.cv data data processing landscape network networks network security neural networks novel processing reliance security services systems type vulnerability