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.

翻译：在通信与网络安全快速发展的背景下，深度神经网络（DNN）和云服务在数据处理中的日益依赖带来了显著漏洞：即可能被恶意行为者利用的后门。本文方法利用高级张量分解算法——独立向量分析（IVA）、多集典型相关分析（MCCA）和平行因子分析（PARAFAC2）——对预训练DNN的权重进行精细分析，有效区分带后门模型与干净模型。该方法的核心优势在于其领域独立性、对不同网络架构的适应性，以及在无需访问被审查模型训练数据的情况下运行的能力。这不仅确保了跨不同应用场景的通用性，还解决了无需预先了解用于改变网络行为的特定触发机制即可识别后门的难题。我们将检测流水线应用于三个不同的计算机视觉数据集，涵盖图像分类和目标检测任务。结果表明，与现有后门检测方法相比，准确率和效率均有显著提升。这一进展增强了深度学习和AI在网络化系统中的安全性，为新兴技术中不断演变的威胁提供了关键网络安全防护。