Occlusion perturbation presents a significant challenge in person re-identification (re-ID), and existing methods that rely on external visual cues require additional computational resources and only consider the issue of missing information caused by occlusion. In this paper, we propose a simple yet effective framework, termed Erasing, Transforming, and Noising Defense Network (ETNDNet), which treats occlusion as a noise disturbance and solves occluded person re-ID from the perspective of adversarial defense. In the proposed ETNDNet, we introduce three strategies: Firstly, we randomly erase the feature map to create an adversarial representation with incomplete information, enabling adversarial learning of identity loss to protect the re-ID system from the disturbance of missing information. Secondly, we introduce random transformations to simulate the position misalignment caused by occlusion, training the extractor and classifier adversarially to learn robust representations immune to misaligned information. Thirdly, we perturb the feature map with random values to address noisy information introduced by obstacles and non-target pedestrians, and employ adversarial gaming in the re-ID system to enhance its resistance to occlusion noise. Without bells and whistles, ETNDNet has three key highlights: (i) it does not require any external modules with parameters, (ii) it effectively handles various issues caused by occlusion from obstacles and non-target pedestrians, and (iii) it designs the first GAN-based adversarial defense paradigm for occluded person re-ID. Extensive experiments on five public datasets fully demonstrate the effectiveness, superiority, and practicality of the proposed ETNDNet. The code will be released at \url{https://github.com/nengdong96/ETNDNet}.

翻译：遮挡扰动对行人重识别（re-ID）构成重大挑战，现有依赖外部视觉线索的方法不仅需要额外计算资源，且仅考虑遮挡造成的缺失信息问题。本文提出一种简单而有效的框架——擦除、变换与加噪防御网络（ETNDNet），将遮挡视为噪声扰动，从对抗防御的角度解决遮挡行人重识别问题。在所提出的ETNDNet中，我们引入三种策略：首先，随机擦除特征图以生成具有不完整信息的对抗性表示，通过身份损失的对抗学习保护re-ID系统免受缺失信息干扰；其次，引入随机变换模拟遮挡造成的位置偏移，以对抗方式训练特征提取器和分类器，学习对错位信息具有鲁棒性的表征；第三，使用随机值扰动特征图以应对障碍物及非目标行人引入的噪声信息，并通过re-ID系统的对抗博弈增强其对遮挡噪声的抵抗能力。无需繁琐设计，ETNDNet具有三个关键亮点：（i）无需任何含参数的外部模块，（ii）有效处理障碍物与非目标行人遮挡引发的各类问题，（iii）设计了首个基于GAN的对抗防御范式用于遮挡行人重识别。在五个公开数据集上的大量实验充分证明了所提ETNDNet的有效性、优越性与实用性。代码将在\url{https://github.com/nengdong96/ETNDNet}开源。