{"type":"ScholarlyArticle","title":"Detector Collapse: Backdooring Object Detection to Catastrophic Overload or Blindness in the Physical World","authors":[{"name":"Hangtao Zhang","slug":"hangtao-zhang","given":"Hangtao","family":"Zhang","affiliation":"School of Cyber Science and Engineering , Huazhong University of Science and Technology; Hubei Engineering Research Center on Big Data Security; Hubei Key Laboratory of Distributed System Security"},{"name":"Shengshan Hu","slug":"shengshan-hu","given":"Shengshan","family":"Hu","affiliation":"School of Cyber Science and Engineering , Huazhong University of Science and Technology; National Engineering Research Center for Big Data Technology and System; Services Computing Technology and System Lab; Hubei Engineering Research Center on Big Data Security; Hubei Key Laboratory of Distributed System Security"},{"name":"Yichen Wang","slug":"yichen-wang","given":"Yichen","family":"Wang","affiliation":"School of Cyber Science and Engineering , Huazhong University of Science and Technology; National Engineering Research Center for Big Data Technology and System; Services Computing Technology and System Lab; Hubei Engineering Research Center on Big Data Security; Hubei Key Laboratory of Distributed System Security"},{"name":"Leo Yu Zhang","slug":"leo-yu-zhang","given":"Leo Yu","family":"Zhang","affiliation":"School of Information and Communication Technology , Griffith University"},{"name":"Ziqi Zhou","slug":"ziqi-zhou","given":"Ziqi","family":"Zhou","affiliation":"School of Computer Science and Technology , Huazhong University of Science and Technology; National Engineering Research Center for Big Data Technology and System; Services Computing Technology and System Lab; Cluster and Grid Computing Lab"},{"name":"Xianlong Wang","slug":"xianlong-wang","given":"Xianlong","family":"Wang","affiliation":"School of Cyber Science and Engineering , Huazhong University of Science and Technology; National Engineering Research Center for Big Data Technology and System; Services Computing Technology and System Lab; Hubei Engineering Research Center on Big Data Security; Hubei Key Laboratory of Distributed System Security"},{"name":"Yanjun Zhang","slug":"yanjun-zhang","given":"Yanjun","family":"Zhang","affiliation":"University of Technology Sydney"},{"name":"Chao Chen","slug":"chao-chen","given":"Chao","family":"Chen","affiliation":"RMIT University"}],"abstract":"Object detection tasks, crucial in safety-critical systems like autonomous driving, focus on pinpointing object locations. These detectors are known to be susceptible to backdoor attacks. However, existing backdoor techniques have primarily been adapted from classification tasks, overlooking deeper vulnerabilities specific to object detection. This paper is dedicated to bridging this gap by introducing Detector Collapse (DC), a brand-new backdoor attack paradigm tailored for object detection. DC is designed to instantly incapacitate detectors (i.e., severely impairing detector's performance and culminating in a denial-of-service). To this end, we develop two innovative attack schemes: Sponge for triggering widespread misidentifications and Blinding for rendering objects invisible. Remarkably, we introduce a novel poisoning strategy exploiting natural objects, enabling DC to act as a practical backdoor in real-world environments. Our experiments on different detectors across several benchmarks show a significant improvement (~10%-60% absolute and ~2-7x relative) in attack efficacy over state-of-the-art attacks.","doi":"10.24963/ijcai.2024/185","url":"https://ijcai.org/proceedings/2024/185","pdf":"https://ijcai.org/proceedings/2024/0185.pdf","markdown":"https://ijcai.org/proceedings/2024/185.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/185","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":185,"pages":{"first":1670,"last":1678},"keywords":["AI Ethics, Trust, Fairness: ETF: Safety and robustness","Computer Vision: CV: Recognition (object detection, categorization)","AI Ethics, Trust, Fairness: ETF: Trustworthy AI"],"videos":[],"references":[{"position":1,"raw":"[Chan et al., 2022] Shih-Han Chan, Yinpeng Dong, Jun Zhu, Xiaolu Zhang, and Jun Zhou. 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