计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (11): 349-363.doi: 10.11896/jsjkx.241200151

• 信息安全 • 上一篇    下一篇

面向可见光与红外多模态目标检测的对抗攻防综述

郑海斌1,2,3, 林秀豪1, 陈靖文1, 陈晋音1,3   

  1. 1 浙江工业大学信息工程学院 杭州 310000
    2 四川大学数据安全防护与智能治理教育部重点实验室 成都 610000
    3 浙江工业大学计算机科学与技术学院、软件学院 杭州 310000
  • 收稿日期:2024-12-19 修回日期:2025-04-23 出版日期:2025-11-15 发布日期:2025-11-06
  • 通讯作者: 陈晋音(chenjinyin@zjut.edu.cn)
  • 作者简介:(haibinzheng320@gmail.com)
  • 基金资助:
    国家自然科学基金(62406286);浙江省自然科学基金(LDQ23F020001); 四川大学数据安全防护与智能治理教育部重点实验室放课题(SCUSAKFKT202402Z);北京生命科技研究院有限公司开放基金(2024200CD0210)

Survey of Adversarial Attack and Defense for RBG and Infrared Multimodal Object Detection

ZHENG Haibin1,2,3, LIN Xiuhao1, CHEN Jingwen1, CHEN Jinyin1,3   

  1. 1 School of Information Engineering,Zhejiang University of Technology,Hangzhou 310000,China
    2 Key Laboratory of Data Protection and Intelligent Management Ministry of Education,Sichuan University,Chengdu 610000,China
    3 College of Computer Science and Technology,College of Software,Zhejiang University of Technology,Hangzhou 310000,China
  • Received:2024-12-19 Revised:2025-04-23 Online:2025-11-15 Published:2025-11-06
  • About author:ZHENG Haibin,born in 1995,Ph.D,lecturer.His main research interests include deep learning and artificial intelligence security.
    CHEN Jinyin,born in 1982,Ph.D,professor.Her main research interests include artificial intelligence security,graph data mining and evolutionary computing.
  • Supported by:
    National Natural Science Foundation of China(62406286), Zhejiang Provincial Natural Science Foundation(LDQ23F020001),Key Laboratory of Data Protection and Intelligent Management, Ministry of Education, Sichuan University(SCUSAKFKT202402Z) and Beijing Life Science Academy(BLSA)(2024200CD0210).

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摘要： 目标检测作为计算机视觉中的一项基本任务被广泛应用,而基于深度学习的目标检测算法以其强大的特征提取能力,成为了当前研究的主流。然而大多数目标检测算法仅对可见光图像或红外图像进行单模态检测。通常情况下,可见光图像在天气恶劣、夜间、目标被遮挡等场景成像较差,导致检测性能下降。利用红外图像可以改善上述问题,但红外图像会缺失目标的部分细节信息。因此,基于可见光和红外图像的多模态融合检测算法逐渐兴起。然而,现有的研究集中于改善多模态目标检测算法的性能,对于其安全性的研究相对零散。基于现有的研究工作,围绕多模态目标检测对抗安全性进行综述。首先对多模态目标检测及攻防进行理论分析;然后按照不同时段的融合检测对多模态目标检测方法进行分类归纳,再对现有的目标检测对抗攻击方法与对抗防御方法进行归纳整理,梳理了现有的多模态目标检测数据集与主要评价指标;最后探讨了多模态目标检测未来潜在的研究方向,进一步推动多模态目标检测对抗安全研究发展和应用。

关键词: 目标检测, 深度学习, 多模态目标检测, 对抗攻击, 防御

Abstract: Object detection,as a fundamental classic task in the field of computer vision,has a wide range of applications.Deep learning based object detection algorithms have become the mainstream of current research due to their superior performance.However,most object detection algorithms only perform single-mode detection on visible or infrared images.In general,visible images have poor imaging in harsh weather,nighttime,and scenes,where targets are obstructed,leading to a decrease in detection performance.The use of infrared images can improve the above issues,but infrared images may miss some details of the target.Therefore,multimodal fusion detection algorithms based on visible light and infrared images are gradually emerging.However,existing research has focused on improving the performance of multimodal object detection algorithms,and research on their security is relatively scattered.Based on existing research work,this paper provides an overview of the security of multimodal object detection in adversarial situations.Firstly,a theoretical analysis of multimodal object detection and attack and defense is conducted.Secondly,multimodal object detection methods are classified and summarized according to fusion detection in different time periods.Then,existing methods of object detection and adversarial defense are summarized and organized,and the existing dataset and main evaluation indicators of multimodal object detection are summarized.Finally,potential research directions for multi-modal object detection in the future are discussed,further promoting the development and application of multimodal object detection in adversarial security research.

Key words: Object detection, Deep learning, Multimodal object detection, Adversarial attacks, Defense

中图分类号: 

  • TP391.4
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