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Creators/Authors contains: "Akin, O"

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  1. ; ; ; (, International Workshop on Artificial Intelligence for Healthcare Applications (AIHA), 2020.)
    null (Ed.)
    The recent advances in algorithmic photo-editing and the vulnerability of hospitals to cyberattacks raises the concern about the tampering of medical images. This paper introduces a new large scale dataset of tampered Computed Tomography (CT) scans generated by different methods, LuNoTim-CT dataset, which can serve as the most comprehensive testbed for comparative studies of data security in healthcare. We further propose a deep learning-based framework, ConnectionNet, to automatically detect if a medical image is tampered. The proposed ConnectionNet is able to handle small tampered regions and achieves promising results and can be used as the baseline for studies of medical image tampering detection. 
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    Accepted Manuscript Full Text Available