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

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.