Pixel-wise adversarial patches are computationally heavy and often visually detectable, limiting utility in security-critical systems. We present adversarial Voronoi camouflage that optimizes only seed-point locations under fixed, printable palettes using a soft assignment, producing structured, splinter camouflage-like patterns without additional regularization. Evaluated on person detection with COCO-style AP@[.5:.95], naive placement (Inria -> COCO) performs comparably bad, while garment-level application via segmentation mask (3DPeople) results in a significant AP drop. The attack transfers to out-of-domain backgrounds and across detector families (YOLOv9/10/11/12), indicating robustness in black-box settings. Repainting with different palettes largely nullifies the effect, and single-color tweaks show limited tolerance (<=0.17), highlighting a structure-palette coupling. The parameter-efficient, palette-constrained design improves visual plausibility while degrading real-time detector performance. Physical validation and color calibration are left for future work. Code: https://github.com/JensBayer/Voronoi This paper was originally presented at the International Conference on Military Communication and Information Systems (ICMCIS), organized by the Information Systems Technology (IST) Scientific and Technical Committee, IST-224-RSY - the ICMCIS, held in Bath, United Kingdom, 12-13 May 2026.

翻译：像素级对抗补丁计算开销大且视觉上往往可察觉，限制了其在安全关键系统中的应用。我们提出一种基于Voronoi图的对抗伪装方法，该方法通过软分配机制仅优化种子点位置，在固定可打印调色板下生成类碎片迷彩的结构化图案，无需额外正则化。在基于COCO风格AP@[.5:.95]指标的人体检测评估中：朴素贴放（Inria → COCO）表现同样较差，而通过分割掩码（3DPeople）进行服装级贴放则导致AP显著下降。该攻击可迁移至域外背景及不同检测器系列（YOLOv9/10/11/12），表明其在黑盒场景下具有鲁棒性。通过不同调色板重涂可基本抵消攻击效果，单色微调容忍度有限（≤0.17），凸显了结构-调色板的耦合性。这种参数高效、受调色板约束的设计在降低实时检测器性能的同时提升了视觉合理性。物理验证与颜色校准留待未来工作。代码：https://github.com/JensBayer/Voronoi 本文最初发表于由信息系统技术（IST）科学技术委员会组织的军事通信与信息系统国际会议（ICMCIS，IST-224-RSY专题研讨会），该会议于2026年5月12-13日在英国巴斯举行。