Investigation And Development Of Convolutional Neural Network Based Image Splicing Detection

Md Hasim, Siti Mastura (2017) Investigation And Development Of Convolutional Neural Network Based Image Splicing Detection. Masters thesis, Universiti Sains Malaysia.

[img]
Preview
PDF - Submitted Version
Download (902Kb) | Preview

    Abstract

    Image splicing detection is an area of studies that have been studied widely all around the world recently. The importance to do image splicing detection is not only for the authorities but also for common user. Image splicing detection requires several steps to be completed and a huge dataset is needed to be used. This study is aimed to investigate and develop CNN based method for image splicing detection. Three preliminary experiments are done according to previous work to observe how pre-processing affects CNN performance. Based on the preliminary experiments, an architecture with reduced number of CNN layers are proposed without any pre-processing. Ten-fold cross validation is used to demonstrate CNN performance. Preliminary experiments shows that CNN performance are critically affected by input image size. Therefore, the proposed architecture are tested with different input image sizes. Three different input image sizes are tested which are 28×28 pixel, 64×64 pixel and 128×128 pixels. From cross validation is can be concluded that 64×64 pixels input image is the most suitable input image size for CNN image splicing detection. At the end of this study, it is observed that by using the proposed architecture, CNN can be used for image splicing detection without any pre-processing.

    Item Type: Thesis (Masters)
    Subjects: T Technology > TK Electrical Engineering. Electronics. Nuclear Engineering > TK1-9971 Electrical engineering. Electronics. Nuclear engineering
    Divisions: Kampus Kejuruteraan (Engineering Campus) > Pusat Pengajian Kejuruteraaan Elektrik & Elektronik (School of Electrical & Electronic Engineering) > Thesis
    Depositing User: Mr Mohd Fadli Abd Rahman
    Date Deposited: 15 Mar 2018 09:57
    Last Modified: 17 May 2018 11:09
    URI: http://eprints.usm.my/id/eprint/39554

    Actions (login required)

    View Item
    Share