分形伪装：一种面向红外域多尺度对抗攻击的仿生方法
Fractal Camouflage: A Bio-Inspired Approach for Multi-Scale Adversarial Attacks in the Infrared Domain
用 H 型分形的自相似性构造跨尺度物理对抗贴片，并通过粒子群优化搜索参数，为红外行人检测器的物理攻击评测提供了可复用的多尺度扰动生成范式；做红外或多光谱检测安全评测时，可直接借鉴其分形参数化与黑盒优化流程。
原文依据 we propose AdvFractal, a black-box attack that exploits the innate self-similarity and structural richness of fractal geometry
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论红外行人检测在安全关键系统中至关重要，但仍易受对抗攻击影响。现有物理攻击往往依赖固定的静态图案。然而，这些方法通常缺乏跨尺度的鲁棒性，因为其手工设计或均匀生成的结构从根本上受限于固定的感受野，无法适应变化的距离与场景上下文。鉴于此，我们提出 AdvFractal，一种利用分形几何固有的自相似性与结构丰富性来自然生成多尺度、物理可实现的对抗扰动的黑盒攻击。通过以 H 型分形对扰动建模并利用粒子群优化算法优化参数，AdvFractal 能够无缝协调跨尺度的攻击，从局部纹理到全局形状逐步破坏检测器特征。实验表明，AdvFractal 在物理域取得 97.54% 的攻击成功率（ASR），跨数据集达到 99.16%，显著优于最优方法。该扰动在红外光谱中高度有效，同时在可见光下保持隐蔽，为评估和理解红外检测系统的安全性提供了新途径。
英文原文摘要 1259 字符
Infrared pedestrian detection is crucial in safety-critical systems but remains vulnerable to adversarial attacks. Existing physical attacks often rely on fixed, static patterns. However, they often lack robustness across scales, as their hand-crafted or uniformly generated structures are fundamentally limited by a fixed receptive field and fail to adapt to varying distances and scene contexts. In light of this, we propose AdvFractal, a black-box attack that exploits the innate self-similarity and structural richness of fractal geometry to naturally generate multi-scale, physically realizable adversarial perturbations. By modeling perturbations with H-type fractals and optimizing parameters via Particle Swarm Optimization, AdvFractal seamlessly coordinates attacks across scales, progressively disrupting detector features from local textures to global shapes. Experiments show AdvFractal achieves an attack success rate (ASR) of 97.54% in the physical domain and 99.16% cross-dataset, significantly outperforming state-of-the-art methods. The perturbations are highly effective in the infrared spectrum while remaining stealthy in visible light, offering a novel approach for evaluating and understanding the security of infrared detection systems.