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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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WRAP-provenance-analysis-instagram-photos-Li-2018.pdf - Accepted Version - Requires a PDF viewer. Download (847Kb) | Preview |
Official URL: https://doi.org/10.1007/978-981-13-6661-1_29
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) | ||||||||
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| Subjects: | H Social Sciences > HM Sociology Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TR Photography |
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| 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: |
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| Date of first compliant deposit: | 19 May 2020 | ||||||||
| 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: |
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| 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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