A Survey and Evaluation of Adversarial Attacks for Object Detection

• URL: http://arxiv.org/abs/2408.01934v2
• Date: Tue, 6 Aug 2024 02:39:46 GMT
• Title: A Survey and Evaluation of Adversarial Attacks for Object Detection
• Authors: Khoi Nguyen Tiet Nguyen, Wenyu Zhang, Kangkang Lu, Yuhuan Wu, Xingjian Zheng, Hui Li Tan, Liangli Zhen,
• Abstract summary: Deep learning models excel in various computer vision tasks but are susceptible to adversarial examples-subtle perturbations in input data that lead to incorrect predictions. This vulnerability poses significant risks in safety-critical applications such as autonomous vehicles, security surveillance, and aircraft health monitoring.
• Score: 11.48212060875543
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning models excel in various computer vision tasks but are susceptible to adversarial examples-subtle perturbations in input data that lead to incorrect predictions. This vulnerability poses significant risks in safety-critical applications such as autonomous vehicles, security surveillance, and aircraft health monitoring. While numerous surveys focus on adversarial attacks in image classification, the literature on such attacks in object detection is limited. This paper offers a comprehensive taxonomy of adversarial attacks specific to object detection, reviews existing adversarial robustness evaluation metrics, and systematically assesses open-source attack methods and model robustness. Key observations are provided to enhance the understanding of attack effectiveness and corresponding countermeasures. Additionally, we identify crucial research challenges to guide future efforts in securing automated object detection systems.

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