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, including 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：对抗性伪装因其能够通过覆盖物体整个表面从任意视角攻击目标检测器而受到关注。然而，现有方法的通用性和鲁棒性往往不足，其迁移性方面常被忽视，导致应用仅限于特定目标且性能有限。为解决这些挑战，我们提出面向可迁移与强效车辆规避的对抗性伪装（ACTIVE），这是一种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，能够使任何3D车辆对检测器隐藏。我们的框架融合了创新性技术以增强通用性和鲁棒性，包括：一种精细化纹理渲染方法，使通用纹理可应用于不同车辆而不受限于特定纹理映射；一种新型隐身损失函数，使车辆无法被检测；以及平滑损失与伪装损失，用于提升对抗性伪装的逼真度。我们在15个不同模型上的大量实验表明，ACTIVE在各种公开检测器（包括最新的YOLOv7）上均持续优于现有工作。值得注意的是，我们的通用性评估揭示了该方法对其他车辆类别、任务（分割模型）以及现实世界具有可观的迁移性，而不仅限于其他车辆。