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Subject [ICASSP 2022] 2 papers have been accepted (Taeheon and Sungjune) in IEEE ICASSP 2022
Name IVY Lab. KAIST
Date 2022-01-24
1. Authors: Taeheon Kim, Hong Joo Lee, and Yong Man Ro
Title: MAP: Multispectral Adversarial Patch to Attack Person Detection
 
Recently, multispectral person detection has shown great performances in real world applications such as autonomous driving and security systems. However, the reliability of person detection against physical attacks has not been fully explored yet in multispectral person detectors. To evaluate the robustness of multispectral person detectors in the physical world, we propose a novel Multispectral Adversarial Patch (MAP) generation framework. MAP is optimized with a Cross-spectral Mapping(CSM) and Material Emissivity(ME) loss. This paper is the first to evaluate the reliability of a multispectral person detector against physical attack. Throughout experiments, our proposed adversarial patch successfully attacks the person detector and the Average Precision (AP) score is dropped by 90.79% in digital space and 73.34% in physical space.
 

2. Authors: Sungjune Park, Dae Hwi Choi, Jung Uk Kim, and Yong Man Ro
Title: Robust Thermal Infrared Pedestrian Detection by Associating Visible Pedestrian Knowledges
 
Recently, pedestrian detection on thermal infrared images has shown the robust pedestrian detection performance. In this paper, we propose a novel thermal infrared pedestrian detection framework which can associate and utilize the complementary pedestrian knowledge from visible images. Motivated by that humans can associate useful information from other sensors to perform a more reliable decision, we devise a Visible-sensory Pedestrian Associating (VPA) Memory to conduct the robust pedestrian detection by utilizing complementary visible-sensory pedestrian knowledge explicitly. The VPA Memory is trained to store the pedestrian information of visible images and associate it with a given thermal infrared pedestrian knowledge via the memory associating learning. We verify the effectiveness of the proposed framework by conducting extensive experiments, and it achieves state-of the-art pedestrian detection performances on thermal infrared images.
 
 
IMAGE VIDEO SYSTEM (IVY.) KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY (KAIST), ICASSP 2022