CMFD using a Novel Localisation Technique and CNN based Classification
Ritu Agarwal1, Om Prakash Verma2
1Ritu Agarwal*, Assistant Professor, Department of Information Technology ,Delhi Technological University,(formerly Delhi College of Engineering), Delhi India.
2Om Prakash Verma, Professor, Delhi Technological University India.
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 6734-6737 | Volume-9 Issue-1, October 2019 | Retrieval Number: A2019109119/2019©BEIESP | DOI: 10.35940/ijeat.A2019.109119
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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: Latest trends of the image processing software, the growth of image manipulation is at peak. To detect the use of such software on an image is a growing research anomaly. This paper proposes a novel copy-move forgery localization approach in an image through a blind approach with no prior information available to the algorithm. Here, we have split the image into equal size blocks and extracted SIFT features for every block. The center of mass for each block is calculated after applying the Gaussian filter. Finally, image features are matched based on the KNN algorithm for CMF localization. However, for classification, the localisation mask is created for the dataset, and is used to train a Convolutional neural networks(CNN) and this trained CNN in turn is used for classification of images as authentic or tampered.
Keywords: Image Forgery, Copy move forgery detection, Convolutional Neural Network, Image Segmentation, SIFT.