Adversarial camouflage has garnered attention for its ability to attack object detectors from any viewpoint by covering the entire object's surface. However, universality and robustness in existing methods often fall short as the transferability aspect is often overlooked, thus restricting their application only to a specific target with limited performance. To address these challenges, we present Adversarial Camouflage for Transferable and Intensive Vehicle Evasion (ACTIVE), a state-of-the-art physical camouflage attack framework designed to generate universal and robust adversarial camouflage capable of concealing any 3D vehicle from detectors. Our framework incorporates innovative techniques to enhance universality and robustness: a refined texture rendering that enables common texture application to different vehicles without being constrained to a specific texture map, a novel stealth loss that renders the vehicle undetectable, and a smooth and camouflage loss to enhance the naturalness of the adversarial camouflage. Our extensive experiments on 15 different models show that ACTIVE consistently outperforms existing works on various public detectors, including the latest YOLOv7. Notably, our universality evaluations reveal promising transferability to other vehicle classes, tasks (segmentation models), and the real world, not just other vehicles.

翻译：对抗性伪装因其通过覆盖物体整个表面从任意视角攻击目标检测器的能力而受到关注。然而，现有方法的通用性与鲁棒性往往不足，因为迁移性方面常被忽视，从而限制其仅能针对特定目标且性能有限。为应对这些挑战，我们提出了可迁移密集车辆规避对抗性伪装（ACTIVE），这是一个先进的物理伪装攻击框架，旨在生成通用且鲁棒的对抗性伪装，使任何3D车辆对检测器不可见。该框架融合了多项创新技术以增强通用性与鲁棒性：精细化纹理渲染技术，可对不同车辆应用通用纹理而不受限于特定纹理映射；新颖的隐蔽损失函数，使车辆无法被检测；以及平滑与伪装损失函数，以提升对抗性伪装的自然度。我们在15种不同模型上的大量实验表明，ACTIVE在各类公开检测器（包括最新YOLOv7）上始终优于现有工作。值得注意的是，通用性评估揭示了对其他车辆类别、任务（分割模型）乃至真实世界具有前景的迁移性，而不仅限于其他车辆。