Hiding Your Face Is Not Enough: user identity linkage with image recognition

• Published in: Social network analysis and mining 2020-12, Vol.10 (1), p.56, Article 56
• Main Authors: Ranaldi, Leondardo, Zanzotto, Fabio Massimo
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Applications of Graph Theory and Complex Networks; Clustering; Computer Science; Data Mining and Knowledge Discovery; Decision trees; Economics; Extraction; Game Theory; Graphs; Humanities; Identity; Law; Methodology of the Social Sciences; Methods; Names; Original Article; Privacy; Social and Behav. Sciences; Social networks; Statistics for Social Sciences; Success; Support vector machines; User behavior; User profiles
• ISSN: 1869-5450; 1869-5469
• DOI: 10.1007/s13278-020-00673-4

People tend to have multiple identities or personalities in their real and on-line lives. In the real life, these identities can be even associated with different names used with parents, groups of friends or in formal contexts. In the on-line side of life, the attitude has exploded: people have the possibility to express different identities with different names in different social networks (SNs), interfacing with these tools claiming the same meaning as the actions and connections in real life. Thus, a fundamental question arises— Can profiles of the same user be connected in multiple SNs? In this paper, we present Hiding Your Face Is Not Enough (HYFINE) model: a User Identity Linking model that fully exploits images in profiles. Our HYFINE model consists of two parts: (1) the corpus extraction system; (2) the classification system HYFINE - c , which classify if two profiles to determine if these profiles are two different identities of the same user by fully using images along with other features. We show that HYFINE model, exploiting images in profiles, can match profiles of the users in different SNs with high performance.