nach oben

Social Network Analysis and Mining

01.12.2020 | Original Article

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

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

https://www.twitter.com.

https://www.facebook.com.

https://www.instagram.com.

https://www.linkedin.com.

The system is available on https://github.com/leooJo/SeleniumWebScraper.

https://en.wikipedia.org/wiki/List_of_most-followed_Twitter_accounts.

https://en.wikipedia.org/wiki/List_of_most-followed_Facebook_pages.

https://en.wikipedia.org/wiki/List_of_most-followed_Instagram_accounts.

https://www.seleniumhq.org/.

www.unece.org.

http://opencv.org/.

The implementation is available in PythonBook format at the following link: https://github.com/leooJo/SeleniumWebScraper

Ahmad W, Ali R (2018) Understanding the users personal attributes selection tendency across social networks, pp 1–6. https://doi.org/10.1109/IoT-SIU.2018.8519905

Cohen WW, Ravikumar P, Fienberg SE (2003) A comparison of string distance metrics for name-matching tasks. In: Proceedings of the 2003 international conference on Information Integration on the Web, IIWEB’03. AAAI Press, pp 73–78. http://dl.acm.org/citation.cfm?id=3104278.3104293

Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297. https://doi.org/10.1023/A:1022627411411CrossRefMATH

Goga O (2014) Matching user accounts across online social networks: methods and applications (corrélation des profils d’utilisateurs dans les réseaux sociaux : méthodes et applications)

Halimi A, Ayday E (2017) Profile matching across unstructured online social networks: threats and countermeasures. CoRR arXiv:1711.01815

Hamming RW (1950) Error detecting and error correcting codes. Bell Syst Tech J 29(2):147–160MathSciNetCrossRef

Kaushal R, Gupta S, Kumaraguru P (2020) Investigation of biases in identity linkage datasets. In: Hung C, Cerný T, Shin D, Bechini A (eds) SAC ’20: the 35th ACM/SIGAPP Symposium on Applied Computing, online event, [Brno, Czech Republic], March 30–April 3, 2020. ACM, pp 1861–1868. https://doi.org/10.1145/3341105.3374015

King DE (2009) Dlib-ml: a machine learning toolkit. J Mach Learn Res 10:1755–1758

Korula N, Lattanzi S (2014) An efficient reconciliation algorithm for social networks. Proc VLDB Endow 7(5):377–388. https://doi.org/10.14778/2732269.2732274CrossRef

Lee RK, Hee MS, Prasetyo PK, Lim E (2019) Linky: visualizing user identity linkage results for multiple online social networks. CoRR arXiv:1902.08737

Levenshtein VI (1966) Binary codes capable of correcting deletions, insertions and reversals. Sov Phys Dokl 10(8):707–710 (doklady Akademii Nauk SSSR, V163 No4 845–848 1965)

Liu S, Wang S, Zhu F, Zhang J, Krishnan R (2014) Hydra: large-scale social identity linkage via heterogeneous behavior modeling. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, SIGMOD ’14. ACM, New York, NY, USA, pp 51–62. https://doi.org/10.1145/2588555.2588559

Lovdata (2019) Imagehash library. https://pypi.org/project/ImageHash/

Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110. https://doi.org/10.1023/B:VISI.0000029664.99615.94CrossRef

Mishra R (2019) Entity resolution in online multiple social networks (@Facebook and LinkedIn). In: Proceedings of IEMIS 2018, vol 2, pp 221–237. https://doi.org/10.1007/978-981-13-1498-8_20

Narayanan A, Shmatikov V (2009) De-anonymizing social networks. In: Proceedings of the 2009 30th IEEE Symposium on Security and Privacy, SP ’09. IEEE Computer Society, Washington, DC, USA, pp 173–187. https://doi.org/10.1109/SP.2009.22

Nunes A, Calado P, Martins B (2012) Resolving user identities over social networks through supervised learning and rich similarity features. In: Proceedings of the 27th Annual ACM Symposium on Applied Computing, SAC ’12. ACM, New York, NY, USA, pp 728–729. https://doi.org/10.1145/2245276.2245413

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830MathSciNetMATH

Peled O, Fire M, Rokach L, Elovici Y (2016) Matching entities across online social networks. Neurocomputing 210(C):91–106. https://doi.org/10.1016/j.neucom.2016.03.089CrossRef

Quinlan JR (1993) C4.5: Programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco

Shu K, Wang S, Tang J, Zafarani R, Liu H (2017) User identity linkage across online social networks: a review. SIGKDD Explor Newsl 18(2):5–17. https://doi.org/10.1145/3068777.3068781CrossRef

Tichy W (1984) The string-to-string correction problem with block moves. ACM Trans Comput Syst 2:309–321. https://doi.org/10.1145/357401.357404CrossRef

Vosecky J, Hong D, Shen V (2009) User identification across multiple social networks, pp 360–365. https://doi.org/10.1109/NDT.2009.5272173

Wang Z, Bovik A, Sheikh H (2005) Structural similarity based image quality assessment. In: Digital Video Image Quality and Perceptual Coding, Ser Series in Signal Processing and Communications. https://doi.org/10.1201/9781420027822.ch7

Wang J, Li G, Fe J (2011) Fast-join: an efficient method for fuzzy token matching based string similarity join. In: 2011 IEEE 27th International Conference on Data Engineering, pp 458–469. https://doi.org/10.1109/ICDE.2011.5767865

Wondracek G, Holz T, Kirda E, Kruegel C (2010) A practical attack to de-anonymize social network users. In: 2010 IEEE Symposium on Security and Privacy, pp 223–238. https://doi.org/10.1109/SP.2010.21

Zafarani R, Liu H (2013) Connecting users across social media sites: a behavioral-modeling approach. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’13. ACM, New York, NY, USA, pp 41–49. https://doi.org/10.1145/2487575.2487648

Titel: Hiding Your Face Is Not Enough: user identity linkage with image recognition
Publikationsdatum: 01.12.2020
Erschienen in: Social Network Analysis and Mining / Ausgabe 1/2020
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI: https://doi.org/10.1007/s13278-020-00673-4

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Premium Partner