Digital Image Falsification Detection System for Effective Data Communication
T. Sasilatha1, K.R. Anupriya2, C. Gnana Kousalya3, S. Arun4
1T. Sasilatha, Professor and Dean, Department of EEE, AMET Deemed to be University, Chennai.
2K.R. Anupriya, Research Scholar, Department of EEE, AMET Deemed to be University, Chennai.
3C. Gnana Kousalya, Professor and HOD, St.Joseph‟s Institute of Technology, Chennai.
4S.Arun, Professor, Department of ECE, Prathyusa Engineering College, Chennai.
Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 4081-4086 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B4950129219/2019©BEIESP | DOI: 10.35940/ijeat.B4950.129219
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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: In this proposed system a digital imagefalsification can be identified using the combination of both adaptive over block based segmentation, feature keypointbased feature extraction algorithms(Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF)) and forgery region extraction algorithm. The proposed falsification detection algorithm comprises both block based falsification detection algorithm (adaptive over block based segmentation and block feature matching algorithm) and the keypoint based falsification detection algorithm(forgery region extraction algorithm). Adaptive over block based Segmentation algorithm adaptively segments the input digital image into separate(non overlapped) blocks in irregular manner. Scale Invariant Feature Transform (SIFT) algorithm and Speeded Up Robust Features (SURF) algorithms are used to draw out features from the segmentedblocks as a block features. Then the extracted features are matched with the feature points of other segmented block. If the feature key points are matched with any other feature point presents in the segmented blocks, then the matched feature points are marked as Labeled key Points (LKP), which can be doubted as a forged regions. Finally, the Forgery Region Extraction algorithm can be used to detect the forged region from the input digital image based on the extracted labeled feature points. The experimental outcomesdisplay that the novelfalsification detection system can accomplished the requirements compared with the existing digital imagefalsification detection methods.
Keywords: Falsification, Forgery, SIFT, SURF, Feature key points, Segmentation, Morphological