摘要： 深度学习模型在实际应用中越来越广泛，但容易受到对抗性示例的攻击，近年来，物理对抗性示例成为研究热点。现有的研究方法多专注于提高对抗性示例的攻击性和针对性，但对于不同模型之间的共性研究仍有不足，忽略了对抗性样本的通用性与视觉自然性。为此，本文提出一种基于色彩感知约束的物理对抗性伪装生成方法，提升伪装的转移性和自然性。具体来说，首先对给定的3D汽车模型进行预处理生成多层注意力图，然后利用求得的二进制掩码来分离多层目标注意力，对于给定的连通子图，提取其在纹理中的像素集合，计算其与可打印颜色空间的映射，接着优化注意力和颜色联合损失来获得最佳的对抗性伪装，在处理完所有连通子图后，进行全局一致性优化，避免各个子图间出现突兀的边界或颜色不平滑现象，从而提升视觉上的舒适度。本方法不依赖特定模型结构，具备良好的跨模型迁移能力和实际应用潜力。大量实验表明，基于色彩感知约束的物理对抗性伪装生成方法在数字世界和物理世界中都超过了基线方法。

Abstract: Deep learning models have become increasingly prevalent in practical applications, but they remain vulnerable to adversarial examples. In recent years, physical adversarial examples have emerged as a research hotspot. Most existing approaches focus on enhancing the attack strength and specificity of adversarial examples, yet they often overlook the commonalities across different models, as well as the generalizability and visual naturalness of adversarial samples. To address these limitations, this paper proposes a physical adversarial camouflage generation method based on color perception constraints, aiming to improve the transferability and naturalness of the camouflage. Specifically, the method first preprocesses a given 3D car model to generate multi-layer attention maps. Then, using derived binary masks, it separates attention regions across layers. For each connected subgraph, the corresponding pixel set is extracted from the texture and mapped to the printable color space. Subsequently, a joint loss combining attention and color constraints is optimized to generate the optimal adversarial camouflage. After processing all subgraphs, global consistency optimization is performed to eliminate abrupt boundaries and color discontinuities between subgraphs, thereby enhancing visual comfort. The proposed method is independent of specific model architectures, demonstrating strong cross-model transferability and practical potential. Extensive experiments show that the color-constrained physical adversarial camouflage method outperforms baseline approaches in both digital and physical environments.