2024/03/20 by Shin, Jeongjin · 4 citations
Computer Science · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #Cryptography and Security (cs.CR) #Digital Media Forensic Detection #FOS: Computer and information sciences #Face recognition and analysis #I.4.8 #Physical Unclonable Functions (PUFs) and Hardware Security
paper · pdf · doi:10.48550/arxiv.2405.09550
openalex publication_date 2024/03/20 · openalex created_date 2024/05/18 · openalex updated_date 2026/07/28
Deep learning models have achieved unprecedented performance in the domain of object detection, resulting in breakthroughs in areas such as autonomous driving and security. However, deep learning models are vulnerable to backdoor attacks. These attacks prompt models to behave similarly to standard models without a trigger; however, they act maliciously upon detecting a predefined trigger. Despite extensive research on backdoor attacks in image classification, their application to object detection remains relatively underexplored. Given the widespread application of object detection in critical real-world scenarios, the sensitivity and potential impact of these vulnerabilities cannot be overstated. In this study, we propose an effective invisible backdoor attack on object detection utilizing a mask-based approach. Three distinct attack scenarios were explored for object detection: object disappearance, object misclassification, and object generation attack. Through extensive experiments, we comprehensively examined the effectiveness of these attacks and tested certain defense methods to determine effective countermeasures. Code will be available at https://github.com/jeongjin0/invisible-backdoor-object-detection