Medical Image Forgery Detection using Cnn
K. Premkumar1, J. Preethi2, J. Sandhya3, Inti Satyapriya Harshini4

1Mr.K. Prem Kumar*, Computer Science and Engineering, Sri Manakula Vinayagar Engineering College, Puducherry, India.
2J. Preethi, Computer Science and Engineering, Sri Manakula Vinayagar Engineering College, Puducherry, India.
3J.Sandhya, Computer Science and Engineering, Sri Manakula Vinayagar Engineering College, Puducherry, India.
4Inti Satyapriya Harshini, Computer Science and Engineering, Sri Manakula Vinayagar Engineering College, Puducherry, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on April 10, 2020. | PP: 1325-1329 | Volume-9 Issue-6, April 2020. | Retrieval Number: F3558049620/2020©BEIESP | DOI: 10.35940/ijitee.F3558.049620
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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: With the improvement of the computer technology, image processing techniques have become important in a wide variety of medical applications. Numerous new features have been added to satisfy people. People consult doctors online, without even visiting them. As health is a critical issue, we should take care with full attention and security. This paper proposes a medical image forgery detection system for the identifying whether the image is altered or not. The forgery done on the medical images can lead to various issues that can shake the medical industry this also promotes wrong diagnosis, organ trafficking etc. Hence a group of different forgery detection algorithms is described and by using the Convolution neural networks we can detect and the forged images. This paper also gives a brief outline about the advantages and disadvantages of the existing systems in forgery detection. 
Keywords: Convolution Neural Network, Copy-Move Attack, Forgery Detection, Medical Imaging.
Scope of the Article: Neural Information Processing