Toward Trustworthy Identity Tracing via Multi-attribute Synergistic Identification

作     者：Liu, Decheng Yu, Jiahao Hu, Ruimin Feng, Wenbin 

作者机构：National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China School of Cyber Engineering Xidian University Xi'An710071 China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2023年

核心收录：

主　　题：Entropy 

摘      要：Identity tracing is a technology that uses the selection and collection of identity attributes of the object to be tested to discover its true identity, and it is one of the most important foundational issues in the field of social security prevention. However, traditional identity recognition technologies based on single attributes have difficulty achieving ultimate recognition accuracy, where deep learning-based model always lacks interpretability. Multivariate attribute collaborative identification is a possible key way to overcome mentioned recognition errors and low data quality problems. In this paper, we propose the Trustworthy Identity Tracing (TIT) task and a Multi-attribute Synergistic Identification based TIT framework. We first established a novel identity model based on identity entropy theoretically. The individual conditional identity entropy and core identification set are defined to reveal the intrinsic mechanism of multivariate attribute collaborative identification. Based on the proposed identity model, we propose a trustworthy identity tracing framework (TITF) with multi-attribute synergistic identification to determine the identity of unknown objects, which can optimize the core identification set and provide an interpretable identity tracing process. Actually, the essence of identity tracing is revealed to be the process of the identity entropy value converges to zero. To cope with the lack of test data, we construct a dataset of 1000 objects to simulate real-world scenarios, where 20 identity attributes are labeled to trace unknown object identities. The experiment results conducted on the mentioned dataset show the proposed TITF algorithm can achieve satisfactory identification performance. Furthermore, the proposed TIT task explores the interpretable quantitative mathematical relationship between attributes and identity, which can help expand the identity representation from a single-attribute feature domain to a multi-attribute collaborat