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    Satellite image forgery detection and localization using GAN and One-Class classifier

    Access Status
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    Authors
    Yarlagadda, S.
    Güera, D.
    Bestagini, P.
    Zhu, Maggie
    Tubaro, S.
    Delp, E.
    Date
    2018
    Type
    Journal Article
    
    Metadata
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    Citation
    Yarlagadda, S. and Güera, D. and Bestagini, P. and Zhu, M. and Tubaro, S. and Delp, E. 2018. Satellite image forgery detection and localization using GAN and One-Class classifier. Electronic Imaging. 2018: 214.
    Source Title
    Electronic Imaging
    DOI
    10.2352/ISSN.2470-1173.2018.07.MWSF-214
    ISSN
    2470-1173
    School
    School of Public Health
    URI
    http://hdl.handle.net/20.500.11937/72717
    Collection
    • Curtin Research Publications
    Abstract

    © 2018, Society for Imaging Science and Technology. Current satellite imaging technology enables shooting highresolution pictures of the ground. As any other kind of digital images, overhead pictures can also be easily forged. However, common image forensic techniques are often developed for consumer camera images, which strongly differ in their nature from satellite ones (e.g., compression schemes, post-processing, sensors, etc.). Therefore, many accurate state-of-the-art forensic algorithms are bound to fail if blindly applied to overhead image analysis. Development of novel forensic tools for satellite images is paramount to assess their authenticity and integrity. In this paper, we propose an algorithm for satellite image forgery detection and localization. Specifically, we consider the scenario in which pixels within a region of a satellite image are replaced to add or remove an object from the scene. Our algorithm works under the assumption that no forged images are available for training. Using a generative adversarial network (GAN), we learn a feature representation of pristine satellite images. A one-class support vector machine (SVM) is trained on these features to determine their distribution. Finally, image forgeries are detected as anomalies. The proposed algorithm is validated against different kinds of satellite images containing forgeries of different size and shape.

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