Compared to query-based black-box attacks, transfer-based black-box attacks
do not require any information of the attacked models, which ensures their
secrecy. However, most existing transfer-based approaches rely on ensembling
multiple models to boost the attack transferability, which is time- and
resource-intensive, not to mention the difficulty of obtaining diverse models
on the same task. To address this limitation, in this work, we focus on the
single-model transfer-based black-box attack on object detection, utilizing
only one model to achieve a high-transferability adversarial attack on multiple
black-box detectors. Specifically, we first make observations on the patch
optimization process of the existing method and propose an enhanced attack
framework by slightly adjusting its training strategies. Then, we analogize
patch optimization with regular model optimization, proposing a series of
self-ensemble approaches on the input data, the attacked model, and the
adversarial patch to efficiently make use of the limited information and
prevent the patch from overfitting. The experimental results show that the
proposed framework can be applied with multiple classical base attack methods
(e.g., PGD and MIM) to greatly improve the black-box transferability of the
well-optimized patch on multiple mainstream detectors, meanwhile boosting
white-box performance. Our code is available at
this https URL.

本研究针对单模型迁移黑盒对象检测攻击问题，提出了一种增强型攻击框架以及自举式方法，提高了攻击的传递性和白盒效果。