Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
As artificial intelligence (AI) systems become increasingly integrated into daily life, the robustness of these systems, particularly object detection models, has gained substantial attention. Object detection is crucial in applications ranging from autonomous driving to surveillance. However, these models are vulnerable to adversarial attacks, which can deceive them into making incorrect predictions. This paper introduces a novel approach to generating inference-time adversarial attacks on object detection models using generalized loss functions. We present the Generalized Targeted Object Attacks (GTOA) and the Generalized Heuristic Object Suppression Technique (GHOST) algorithms, which perform targeted and vanishing attacks, respectively. Our method is highly adaptable, allowing attacks on any object detection model with minimal model adjustments. We demonstrate that our generalized loss function-based attacks are effective across various object detection models, highlighting the need for enhanced robustness in AI systems.
Recommended Citation
Datta, Soumil and Walter, Charles, "Generalized Loss-Function-Based Attacks for Object Detection Models" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 2.
https://aisel.aisnet.org/hicss-58/st/safety_trustworthy_and_responsible_ai/2
Generalized Loss-Function-Based Attacks for Object Detection Models
Hilton Waikoloa Village, Hawaii
As artificial intelligence (AI) systems become increasingly integrated into daily life, the robustness of these systems, particularly object detection models, has gained substantial attention. Object detection is crucial in applications ranging from autonomous driving to surveillance. However, these models are vulnerable to adversarial attacks, which can deceive them into making incorrect predictions. This paper introduces a novel approach to generating inference-time adversarial attacks on object detection models using generalized loss functions. We present the Generalized Targeted Object Attacks (GTOA) and the Generalized Heuristic Object Suppression Technique (GHOST) algorithms, which perform targeted and vanishing attacks, respectively. Our method is highly adaptable, allowing attacks on any object detection model with minimal model adjustments. We demonstrate that our generalized loss function-based attacks are effective across various object detection models, highlighting the need for enhanced robustness in AI systems.
https://aisel.aisnet.org/hicss-58/st/safety_trustworthy_and_responsible_ai/2