Year: 2025
Type: article
Abstract: Deep neural networks have advanced the perception and decision-making functions of smart embedded systems, such as car-borne driver assistance. Deploying these embedded neural networks often faces two challenges: (i) security vulnerabilities to adversarial examples that can be deployed in the perceived physical environment; (ii) limited computational resources coupled with dynamic conditions that necessitate real-time adaptation of model execution. However, these two challenges are often addressed separately in existing research. This article presents LeapNet, which aims to address both challenges simultaneously. It comprises two versions: LeapNet-1 and LeapNet-2. LeapNet-1 employs dynamic layer routing to counteract adaptive adversarial-example attacks and reduce computational redundancy. Building upon LeapNet-1, LeapNet-2 further adapts its layer routing configurations in real time to meet the frame processing rate requirements under dynamic conditions while maintaining defense performance. Extensive experiments on various representative datasets, neural network models, and adaptive attacks demonstrate the superiority of LeapNet over existing defense methods. On-road tests with a real-time car-borne traffic sign recognition system validate its effectiveness in maintaining frame processing rate under dynamic conditions.
DOI: 10.1145/3762191
Authors: Zimo Ma, Xiangzhong Luo, Qun Song, Rui Tan
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Award: G5622044399
OpenAlex ID: W4413286952