DOG: An Object Detection Adversarial Attack Method

This study presents an object detection adversarial attack method (DOG) based on the dynamic optimization of a multi-scale feature grid cluster, aimed at addressing the challenges of poor transferability in white-box attacks and long generation cycles in black-box attacks within the current adversarial example generation techniques. The DOG leverages the C&W adversarial example generation framework, DAG model activation value selection, and R-AP object label resetting. It introduces the maximum/minimum dynamic target category biasing, background resetting, and PSNR(Peak Signal-to-Noise Ratio) rollback mechanisms. By constructing a specialized loss function, the algorithm can iteratively generate adversarial noise, enabling the rapid production of adversarial examples with high attack and transfer rates. Experimental results demonstrate that the DOG performs exceptionally well across the three image classification networks and six object detection networks. In white-box conditions, the attack success rate exceeds 97%, whereas in black-box conditions, it reaches over 51%, showing good generality and efficiency. Additionally, ablation experiments were used to analyze the impact of each module on attack effectiveness, providing a theoretical basis and practical guidance for future research. This study offers new ideas and methods for further exploration of adversarial example generation.