  Deep neural networks have significantly advanced person re-identification
(ReID) applications in the realm of the industrial internet, yet they remain
vulnerable. Thus, it is crucial to study the robustness of ReID systems, as
there are risks of adversaries using these vulnerabilities to compromise
industrial surveillance systems. Current adversarial methods focus on
generating attack samples using misclassification feedback from victim models
(VMs), neglecting VM's cognitive processes. We seek to address this by
producing authentic ReID attack instances through VM cognition decryption. This
approach boasts advantages like better transferability to open-set ReID tests,
easier VM misdirection, and enhanced creation of realistic and undetectable
assault images. However, the task of deciphering the cognitive mechanism in VM
is widely considered to be a formidable challenge. In this paper, we propose a
novel inconspicuous and controllable ReID attack baseline, LCYE (Look Closer to
Your Enemy), to generate adversarial query images. Specifically, LCYE first
distills VM's knowledge via teacher-student memory mimicking the proxy task.
This knowledge prior serves as an unambiguous cryptographic token,
encapsulating elements deemed indispensable and plausible by the VM, with the
intent of facilitating precise adversarial misdirection. Further, benefiting
from the multiple opposing task framework of LCYE, we investigate the
interpretability and generalization of ReID models from the view of the
adversarial attack, including cross-domain adaption, cross-model consensus, and
online learning process. Extensive experiments on four ReID benchmarks show
that our method outperforms other state-of-the-art attackers with a large
margin in white-box, black-box, and target attacks. The source code can be
found at https://github.com/MingjieWang0606/LCYE-attack_reid.
