Sparse Adversarial Video Attack Based on Dual-Branch Neural Network on Industrial Artificial Intelligence of Things

• Published in: IEEE transactions on industrial informatics 2024-07, Vol.20 (7), p.9385-9392
• Main Authors: Deng, Wenfeng, Yang, Chunhua, Huang, Keke, Liu, Yishun, Gui, Weihua, Luo, Jun
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Artificial neural networks; Convolutional codes; Deep neural networks (DNNs); dual-branch neural network (DBNet); Effectiveness; Employee welfare; industrial artificial intelligence (IAI); Neck; Neural networks; Optimization; Perturbation methods; sparse adversarial video attack (SAVA); Task analysis; working condition identification
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2024.3383517

Deep neural networks (DNNs) as one of the key enabling technologies have been widely used in industrial artificial intelligence (IAI). However, recent research has revealed that they are quite vulnerable to adversarial attacks, arousing serious concerns about DNNs' robustness in many IAI-driven applications such as industrial video analysis tasks. Considering the attack efficiency and effectiveness, it is essential to study the sparse adversarial attack examples. Nevertheless, current methods' performance is limited by insufficient sparsity and lacks a unified framework. To solve these problems, in this article, we focus on sparse adversarial video attacks and propose a dual-branch neural network-based model to generate sparse adversarial video examples in an end-to-end fashion. We conduct extensive experiments with mainstream video models on public datasets and industrial case. Experimental results demonstrate that compared with state-of-the-art methods, our method can achieve a faster and better attacking performance with less than 1% perturbed pixels in the video.