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On the usage of Sensor Pattern Noise for picture-to-identity linking through social network accounts
KTH, School of Information and Communication Technology (ICT), Communication Systems, CoS.
2014 (English)In: VISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications, 2014, 5-11 p.Conference paper (Refereed)
Abstract [en]

Digital imaging devices have gained an important role in everyone's life, due to a continuously decreasing price, and of the growing interest on photo sharing through social networks. As a result of the above facts, everyone continuously leaves visual "traces" of his/her presence and life on the Internet, that can constitute precious data for forensic investigators. Digital Image Forensics is the task of analysing such digital images for collecting evidences. In this field, the recent introduction of techniques able to extract a unique "fingerprint" of the source camera of a picture, e.g. based on the Sensor Pattern Noise (SPN), has set the way for a series of useful tools for the forensic investigator. In this paper, we propose a novel usage of SPN, to find social network accounts belonging to a certain person of interest, who has shot a given photo. This task, that we name Picture-to-Identity linking, can be useful in a variety of forensic cases, e.g., finding stolen camera devices, cyber-bullying, or on-line child abuse. We experimentally test a method for Picture-to-Identity linking on a benchmark data set of publicly accessible social network accounts collected from the Internet. We report promising result, which show that such technique has a practical value for forensic practitioners.

Place, publisher, year, edition, pages
2014. 5-11 p.
Keyword [en]
Account, Digital image forensics, Identity, Linking, Multimedia forensics, Sensor Pattern Noise, Social network, Image processing, Internet, Statistical tests, Social networking (online)
National Category
Communication Systems
URN: urn:nbn:se:kth:diva-167903ScopusID: 2-s2.0-84906919756ISBN: 9789897580093OAI: diva2:820132
9th International Conference on Computer Vision Theory and Applications, VISAPP 2014, 5 January 2014 through 8 January 2014, Lisbon

QC 20150611

Available from: 2015-06-11 Created: 2015-05-22 Last updated: 2015-06-11Bibliographically approved

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