  Deep learning-based person re-identification (Re-ID) has made great progress
and achieved high performance recently. In this paper, we make the first
attempt to examine the vulnerability of current person Re-ID models against a
dangerous attack method, \ie, the universal adversarial perturbation (UAP)
attack, which has been shown to fool classification models with a little
overhead. We propose a \emph{more universal} adversarial perturbation (MUAP)
method for both image-agnostic and model-insensitive person Re-ID attack.
Firstly, we adopt a list-wise attack objective function to disrupt the
similarity ranking list directly. Secondly, we propose a model-insensitive
mechanism for cross-model attack. Extensive experiments show that the proposed
attack approach achieves high attack performance and outperforms other state of
the arts by large margin in cross-model scenario. The results also demonstrate
the vulnerability of current Re-ID models to MUAP and further suggest the need
of designing more robust Re-ID models.
