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Provenance analysis for instagram photos

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Quan, Yijun, Lin, Xufeng and Li, Chang-Tsun (2019) Provenance analysis for instagram photos. In: 16th Australian Data Mining Conference, Bathurst, Australia, 28-30 Nov 2018. Published in: Data Mining, 996 pp. 372-383. ISBN 9789811366604. ISSN 1865-0929. doi:10.1007/978-981-13-6661-1_29

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Official URL: https://doi.org/10.1007/978-981-13-6661-1_29

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Abstract

As a feasible device fingerprint, sensor pattern noise (SPN) has been proven to be effective in the provenance analysis of digital images. However, with the rise of social media, millions of images are being uploaded to and shared through social media sites every day. An image downloaded from social networks may have gone through a series of unknown image manipulations. Consequently, the trustworthiness of SPN has been challenged in the provenance analysis of the images downloaded from social media platforms. In this paper, we intend to investigate the effects of the pre-defined Instagram images filters on the SPN-based image provenance analysis. We identify two groups of filters that affect the SPN in quite different ways, with Group I consisting of the filters that severely attenuate the SPN and Group II consisting of the filters that well preserve the SPN in the images. We further propose a CNN-based classifier to perform filter-oriented image categorization, aiming to exclude the images manipulated by the filters in Group I and thus improve the reliability of the SPN-based provenance analysis. The results on about 20, 000 images and 18 filters are very promising, with an accuracy higher than 96% in differentiating the filters in Group I and Group II.

Item Type: Conference Item (Paper)
Subjects: H Social Sciences > HM Sociology
Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
T Technology > TR Photography
Divisions: Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH): Image processing -- Digital techniques, Digital images, Social media, Instagram (Firm), Photography -- Digital techniques -- Social aspects, User-generated content
Series Name: Communications in Computer and Information Science
Journal or Publication Title: Data Mining
Publisher: Springer
ISBN: 9789811366604
ISSN: 1865-0929
Official Date: 2019
Dates:
DateEvent
2019Published
16 February 2019Available
29 June 2018Accepted
Volume: 996
Page Range: pp. 372-383
DOI: 10.1007/978-981-13-6661-1_29
Status: Peer Reviewed
Publication Status: Published
Publisher Statement: This is a post-peer-review, pre-copyedit version of an article published in Communications in Computer and Information Science. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-981-13-6661-1_29
Access rights to Published version: Restricted or Subscription Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
690907Horizon 2020 Framework Programmehttp://dx.doi.org/10.13039/100010661
Conference Paper Type: Paper
Title of Event: 16th Australian Data Mining Conference
Type of Event: Conference
Location of Event: Bathurst, Australia
Date(s) of Event: 28-30 Nov 2018
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