Recent research has shown that adversarial patches can manipulate outputs from object detection models. However, the conspicuous patterns on these patches may draw more attention and raise suspicions among humans. Moreover, existing works have primarily focused on the attack performance of individual models and have neglected the generation of adversarial patches for ensemble attacks on multiple object detection models. To tackle these concerns, we propose a novel approach referred to as the More Vivid Patch (MVPatch), which aims to improve the transferability and stealthiness of adversarial patches while considering the limitations observed in prior paradigms, such as easy identification and poor transferability. Our approach incorporates an attack algorithm that decreases object confidence scores of multiple object detectors by using the ensemble attack loss function, thereby enhancing the transferability of adversarial patches. Additionally, we propose a lightweight visual similarity measurement algorithm realized by the Compared Specified Image Similarity (CSS) loss function, which allows for the generation of natural and stealthy adversarial patches without the reliance on additional generative models. Extensive experiments demonstrate that the proposed MVPatch algorithm achieves superior attack transferability compared to similar algorithms in both digital and physical domains, while also exhibiting a more natural appearance. These findings emphasize the remarkable stealthiness and transferability of the proposed MVPatch attack algorithm.

翻译：近期研究表明，对抗性补丁能够操纵目标检测模型的输出结果。然而，这些补丁上醒目的图案可能更易引起人类注意并引发质疑。此外，现有研究主要聚焦于单模型的攻击性能，而忽略了针对多个目标检测模型集成攻击的对抗补丁生成。为解决这些问题，我们提出了一种称为"更逼真补丁"（MVPatch）的新型方法，旨在提升对抗补丁的可迁移性和隐蔽性，同时克服先前范式存在的易识别、可迁移性差等局限性。该方法通过集成攻击损失函数降低多个目标检测器的目标置信度分数，从而增强对抗补丁的可迁移性。此外，我们提出了一种轻量级视觉相似性度量算法，该算法通过比较指定图像相似性（CSS）损失函数实现，无需依赖额外生成模型即可生成自然隐蔽的对抗补丁。大量实验表明，与数字域和物理域中的同类算法相比，所提MVPatch算法在展现更自然外观的同时，实现了更优的攻击可迁移性。这些发现充分证明了所提MVPatch攻击算法卓越的隐蔽性和可迁移性。