  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}.
