Generalized Loss-Function-Based Attacks for Object Detection Models

Abstract

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.