信号处理2025，Vol.41Issue(10)：1670-1680,11.DOI:10.12466/xhcl.2025.10.007
基于对抗训练的图像语义多跳传输策略
Adversarial Training Framework for Multi-Hop Semantic Transmission Against Malicious Attacks
摘要
Abstract
With the rapid integration and development of critical technologies,such as wireless communications and arti-ficial intelligence,various intelligent unmanned devices have been widely deployed in wireless ad-hoc network sce-narios for exploration and data collection tasks.This proliferation has generated substantial volumes of image data,spawning a significant demand for massive data transmission.In dynamic wireless environments,traditional coding and transmission schemes that focus on accurate symbol delivery are gradually encountering bottlenecks in information com-pression,thus proving inadequate to address the core challenge of massive data transmission under constrained band-width.As a new paradigm integrating intelligence and communication,semantic communication delves into the novel dimension of information semantics itself.By adopting the joint source-channel coding(JSCC)approach,it signifi-cantly enhances transmission efficiency and spectrum utilization through semantic-level extraction,compression,and re-construction of data,thereby providing innovative ideas and methodologies for future data communication.However,owing to the node mobility and multi-hop relay characteristics of wireless ad-hoc networks,relay nodes are vulnerable to malicious attacks,such as jamming,tampering,and eavesdropping.These attacks can severely compromise the qual-ity of image data transmission.Therefore,designing an efficient and robust multi-hop transmission mechanism for image transmission is crucial.Existing research on image semantic communication primarily focuses on communication-friendly environments,with little consideration given to the impact of malicious attacks on the image semantic transmis-sion process in wireless ad-hoc networks.Aiming at the poor anti-attack capability of image semantic transmission in wireless ad-hoc network scenarios,this study conducts research on image semantic multi-hop transmission algorithms and proposes a transformer sliding window-based semantic multi-hop transmission framework.By utilizing self-attention mechanisms to achieve efficient extraction of multi-scale semantic features and combining joint source-channel cod-ing methods,it effectively enhances end-to-end transmission efficiency.Furthermore,to address the issue of mali-cious node attacks in wireless ad-hoc networks,this study proposes an adversarial training algorithm designed for se-mantic multi-hop transmission models.The method formulates a loss function for multi-hop image transmission and enhances the anti-attack capability of the proposed algorithm by dynamically adjusting and optimizing the semantic en-coding and decoding strategies at each node within the wireless multi-hop transmission system.This approach effec-tively improves the algorithm's resistance to attacks,while reducing semantic distortion in the received images.More-over,considering the issue of semantic distortion accumulation caused by noise interference in the semantic multi-hop transmission system,a transformer-based denoising module is designed to mitigate semantic distortion resulting from channel noise interference and malicious attacks during the multi-hop transmission process,thereby further improving the robustness of the semantic multi-hop transmission.Simulation results indicate that in the additive white Gaussian noise(AWGN)channel,the proposed semantic multi-hop transmission strategy outperforms baseline methods across metrics,such as peak signal-to-noise ratio(PSNR)and multi-scale structural similarity index measure(MS-SSIM).In both AWGN and Rayleigh channels containing malicious attacks,the proposed semantic multi-hop transmission strategy demonstrates significantly superior image reconstruction quality compared with existing schemes.Experimental results confirm that introducing the image denoising module and adversarial training effectively enhances the robustness of the image semantic multi-hop transmission system.关键词
语义多跳传输/联合信源信道编码/恶意攻击/对抗训练Key words
multi-hop semantic transmission/joint source-channel coding/malicious attack/adversarial training分类
信息技术与安全科学引用本文复制引用
杨雨佳,王雪锦,张娜,刘宜明,张治..基于对抗训练的图像语义多跳传输策略[J].信号处理,2025,41(10):1670-1680,11.基金项目
国家自然科学基金(62293481,62471065) （62293481,62471065）
北京市自然科学基金(L251036) （L251036）
中国科协青年人才托举工程(2023QNRC001) （2023QNRC001）
中央高校基本科研业务费专项资金资助项目(2023RC95) The National Natural Science Foundation of China(62293481,62471065) （2023RC95）
Beijing Natural Science Foundation(L251036) （L251036）
Young Talent Support Project of China Association for Science and Technology(2023QNRC001) （2023QNRC001）
Fundamental Research Funds for the Central Universities(2023RC95) （2023RC95）
