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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.

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Title PRNU-Based Content Forgery Localization Augmented With Image Segmentation
Author(s) Lin, XufengORCID iD for Lin, Xufeng orcid.org/0000-0002-3400-8700
Li, Chang-TsunORCID iD for 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 Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30146444

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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.