Copy-Move Forgery Detection in Digital Images using Neural Network
Jigna J. Patel1, Ninad S. Bhatt2
1Jigna J. Patel*, Gujarat Technological University, Chandkheda, Gujarat, India.
2Dr.Ninad S. Bhatt, Electronics and Communication Department, CKPCET, Surat, Gujarat, India.
Manuscript received on January 13, 2020. | Revised Manuscript received on January 29, 2020. | Manuscript published on February 10, 2020. | PP: 1560-1564 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1716029420/2020©BEIESP | DOI: 10.35940/ijitee.D1716.029420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Due to easy availability of image editing software applications, many of the digital images are tempered, either to hide some important facts of the image or just to enhance the image. Hence, the integrity of the image is compromised. Thus, in order to preserve the authenticity of an image, it is necessary to develop some algorithms to detect counterfeit parts of an image, if there is any. Two kinds of classic methods exist for the detection of forgery: the key- point based method in which major key points of the image is found and forged part is detected and the block based method that locates the forged part by sectioning the whole image into blocks. Unlike these two classic methods that require multiple stages, our proposed CNN solution provides better image forgery detection. Our experimental results revealed a better forgery detection performance than any other classic approaches.
Keywords: Copy-Move Forgery Detection, Convolution Neural Network, Tempered Digital Images, Pixel-Based Image Forgery Detection, Block Based Image Forgery Detection.
Scope of the Article: Digital signal processing theory