  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 effectively. The key strengths of our method lie in
its domain independence, adaptability to various network architectures, and
ability to operate without access to the training data of the scrutinized
models. This not only ensures versatility across different application
scenarios but also addresses the challenge of identifying backdoors without
prior knowledge of the specific triggers employed to alter network behavior. We
have applied our detection pipeline to three distinct computer vision datasets,
encompassing both image classification and object detection tasks. The results
demonstrate a marked improvement in both accuracy and efficiency over existing
backdoor detection methods. This advancement enhances the security of deep
learning and AI in networked systems, providing essential cybersecurity against
evolving threats in emerging technologies.
