PRNU-Based Content Forgery Localization Augmented With Image Segmentation Lin, Xufeng and Li, Chang-Tsun 2020, PRNU-Based Content Forgery Localization Augmented With Image Segmentation, IEEE Access, vol. 8, pp. 222645-222659, doi: 10.1109/access.2020.3042780. Attached Files Name Description MIMEType Size Downloads lin-prnubasedcontentforgery-2020.pdf Published version application/pdf 8.75MB 7 Title PRNU-Based Content Forgery Localization Augmented With Image Segmentation Author(s) Lin, Xufeng orcid.org/0000-0002-3400-8700 Li, Chang-Tsun orcid.org/0000-0003-4735-6138 Journal name IEEE Access Volume number 8 Start page 222645 End page 222659 Total pages 15 Publisher IEEE Place of publication Piscataway, N.J. Publication date 2020-12-07 ISSN 2169-3536 Keyword(s) digital image forensics image forgery localization image segmentation multimedia security multi-orientation detection photo-response non-uniformity noise Summary The advances in image editing and retouching technology have enabled an unskilled person to easily produce visually indistinguishable forged images. To detect and localize such forgeries, many image forensic tools rely on visually imperceptible clues, e.g. the subtle traces or artifacts introduced during image acquisition and processing, and slide a regular, typically square, detection window across the image to search for discrepancies of specific clues. Such a sliding-window paradigm confines the explorable neighborhood to a regular grid and inevitably limits its capability in localizing forgeries in a broad range of shapes. While image segmentation that generates segments adhering to object boundaries might be a promising alternative to the sliding window-based approach, it is generally believed that the potential of the segmentation-based detection scheme is hindered by object removal forgeries where meaningful segments are often unavailable. In this work, we take forgery localization based on photo-response non-uniformity (PRNU) noise as an example and propose a segmentation-based forgery localization scheme that exploits the local homogeneity of visually indiscernible clues to mitigate the limitations of existing segmentation approaches that are merely based on visually perceptible content. We further propose a multi-orientation localization scheme that integrates the forgery probabilities obtained with image segmentation and multi-orientated detection windows. The multi-orientation scheme aggregates the complementary strengths of image segmentation and multi-oriented detection window in localizing the object insert and object removal forgeries. Experimental results on a public realistic tampering image dataset demonstrate that the proposed segmentation-based and multi-orientation forgery localization schemes outperform existing state-of-the-art PRNU-based forgery localizers in terms of both region and boundary F1 scores. Language eng DOI 10.1109/access.2020.3042780 Indigenous content off Field of Research 08 Information and Computing Sciences 09 Engineering 10 Technology HERDC Research category C1 Refereed article in a scholarly journal Free to Read? Yes Use Rights Persistent URL http://hdl.handle.net/10536/DRO/DU:30146444 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Open Access Collection Related Links Link Description Link to full-text (open access)     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Wed, 06 Jan 2021, 09:51:54 EST Thu, 07 Jan 2021, 04:01:26 EST Thu, 07 Jan 2021, 05:05:23 EST Sat, 09 Jan 2021, 01:44:10 EST Thu, 28 Jan 2021, 13:50:22 EST Thu, 28 Jan 2021, 13:51:33 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 19 Abstract Views, 8 File Downloads  -  Detailed Statistics Created: Wed, 06 Jan 2021, 09:51:54 EST

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