Provenance analysis for Instagram photos

Yijun Quan, Xufeng Lin, Chang Tsun Li

Research output: Book chapter/Published conference paperConference paper

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.
Original languageEnglish
Title of host publicationData Mining - 16th Australasian Conference, AusDM 2018, Revised Selected Papers
EditorsYanchang Zhao, Graco Warwick, David Stirling, Chang-Tsun Li, Yun Sing Koh, Rafiqul Islam, Zahidul Islam
PublisherSpringer-Verlag London Ltd.
Pages372-383
Number of pages12
ISBN (Print)9789811366604
DOIs
Publication statusPublished - Feb 2019
Event16th Australasian Conference on Data Mining, AusDM 2018 - Charles Sturt University , Bathurst, Australia
Duration: 28 Nov 201830 Nov 2018
https://ausdm18.ausdm.org/

Publication series

NameCommunications in Computer and Information Science
Volume996
ISSN (Print)1865-0929

Conference

Conference16th Australasian Conference on Data Mining, AusDM 2018
CountryAustralia
CityBathurst
Period28/11/1830/11/18
Internet address

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    Quan, Y., Lin, X., & Li, C. T. (2019). Provenance analysis for Instagram photos. In Y. Zhao, G. Warwick, D. Stirling, C-T. Li, Y. S. Koh, R. Islam, & Z. Islam (Eds.), Data Mining - 16th Australasian Conference, AusDM 2018, Revised Selected Papers (pp. 372-383). (Communications in Computer and Information Science; Vol. 996). Springer-Verlag London Ltd.. https://doi.org/10.1007/978-981-13-6661-1_29