Object detectors have demonstrated vulnerability to adversarial examples crafted by small perturbations that can deceive the object detector. Existing adversarial attacks mainly focus on white-box attacks and are merely valid at a specific viewpoint, while the universal multi-view black-box attack is less explored, limiting their generalization in practice. In this paper, we propose a novel universal multi-view black-box attack against object detectors, which optimizes a universal adversarial UV texture constructed by multiple image stickers for a 3D object via the designed layout optimization algorithm. Specifically, we treat the placement of image stickers on the UV texture as a circle-based layout optimization problem, whose objective is to find the optimal circle layout filled with image stickers so that it can deceive the object detector under the multi-view scenario. To ensure reasonable placement of image stickers, two constraints are elaborately devised. To optimize the layout, we adopt the random search algorithm enhanced by the devised important-aware selection strategy to find the most appropriate image sticker for each circle from the image sticker pools. Extensive experiments conducted on four common object detectors suggested that the detection performance decreases by a large magnitude of 74.29% on average in multi-view scenarios. Additionally, a novel evaluation tool based on the photo-realistic simulator is designed to assess the texture-based attack fairly.

翻译：目标检测器已显示出对由微小扰动构建的对抗样本的脆弱性，这些扰动能够欺骗目标检测器。现有的对抗攻击主要集中于白盒攻击，且仅在特定视角下有效，而通用多视角黑盒攻击的研究相对较少，这限制了其在实践中的泛化能力。本文提出了一种针对目标检测器的新型通用多视角黑盒攻击方法，该方法通过设计的布局优化算法，优化由多个图像贴纸构建的、用于三维物体的通用对抗UV纹理。具体而言，我们将图像贴纸在UV纹理上的放置视为一个基于圆形的布局优化问题，其目标是找到填充图像贴纸的最优圆形布局，使其能够在多视角场景下欺骗目标检测器。为确保图像贴纸的合理放置，我们精心设计了两项约束条件。为优化布局，我们采用随机搜索算法，并结合设计的重要性感知选择策略，从图像贴纸池中为每个圆形选择最合适的图像贴纸。在四种常见目标检测器上进行的大量实验表明，在多视角场景下，检测性能平均大幅下降74.29%。此外，我们设计了一种基于逼真模拟器的新型评估工具，以公平评估基于纹理的攻击效果。