Volume 11, Issue 1, October 2014, Pages 126–129
Liya Baby1 and Ann Jose2
1 Applied Electronics, Ilahia College of Engineering and Technology, Kochi, Kerala, India
2 Electronics and Communication Engineering, Ilahia College of Engineering and Technology, Kochi, Kerala, India
Original language: English
Copyright © 2014 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Nowadays digital images are widely used in our day to day life. Image forgery is the process of manipulation of an image to hide some meaningful information of the image. Today digital image forgery become easy due to the availiability of powerful computers, advanced photo editing softwares so that even ordinary users have more access to the digital doctoring tools than even before. The driving forces for the detection of forgered images are the need of authenticity and to maintain the integrity of the image. In this paper an automatic machine learning method for detecting the forgery due to image composition or splicing is considered. Here GLCM features and edge based features are extracted from the illuminant map of an image and 'then provide to a machine learning approach for obtaining the result. Here we use kNN classifier for classifying the image as whether it is original or forgered.
Author Keywords: Edge detection, GLCM features, illuminant estimator, image splicing detection, kNN classifier, machine learning.
Liya Baby1 and Ann Jose2
1 Applied Electronics, Ilahia College of Engineering and Technology, Kochi, Kerala, India
2 Electronics and Communication Engineering, Ilahia College of Engineering and Technology, Kochi, Kerala, India
Original language: English
Copyright © 2014 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Nowadays digital images are widely used in our day to day life. Image forgery is the process of manipulation of an image to hide some meaningful information of the image. Today digital image forgery become easy due to the availiability of powerful computers, advanced photo editing softwares so that even ordinary users have more access to the digital doctoring tools than even before. The driving forces for the detection of forgered images are the need of authenticity and to maintain the integrity of the image. In this paper an automatic machine learning method for detecting the forgery due to image composition or splicing is considered. Here GLCM features and edge based features are extracted from the illuminant map of an image and 'then provide to a machine learning approach for obtaining the result. Here we use kNN classifier for classifying the image as whether it is original or forgered.
Author Keywords: Edge detection, GLCM features, illuminant estimator, image splicing detection, kNN classifier, machine learning.
How to Cite this Article
Liya Baby and Ann Jose, “Detection of Splicing in Digital Images Based on Illuminant Features,” International Journal of Innovation and Scientific Research, vol. 11, no. 1, pp. 126–129, October 2014.
