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研究生丁文杰的文章被IEEE TIFS期刊(CCF-A)接收
发布者: 洪晓鹏 | 2021-05-06

经过一年半的不懈尝试和修改"Beyond Universal Person Re-Identification Attack"一文终于被CCF-A类期刊IEEE TIFS(IEEE Transactions on Information Forensics & Security)。

该文章是与华为云田奇老师和厦门大学纪荣嵘教授合作文章,恭喜丁文杰同学,恭喜各位合作者。

本文对sota人群重识别模型的脆弱性进行了研究,提出了一种跨模型跨数据集的人群重识别模型攻击噪声生成算法

 

Title: Beyond Universal Person Re-Identification Attack

Arxiv地址:点这里

Authors: Wenjie DingXing WeiRongrong JiXiaopeng HongQi TianYihong Gong

Abstract: 

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.