Abstract
Digital image forgery has become extremely easy as low-cost image processing programs are readily available. Digital image forensics is a science of classifying images as authentic or manipulated. This paper aims at implementing a novel digital image forensics technique by exploiting an image’s Color Channel Characteristics (CCC). The CCCs considered are the noise and edge characteristics of the image. Averaging, median, Gaussian and Wiener filters along with Sobel, Canny, Prewitt and Laplacian of Gaussian (LoG) edge detectors are applied to get the noise and texture features. A complete, no reference, blind classifier for image tamper detection has been proposed and implemented. The proposed CCC classifier can detect copy-move as well as image splicing accurately with lower dimensionality. Support Vector Machine is used for classification of images as authentic or tampered. Experimental results have shown that the proposed technique outperforms the existing ones and may serve as a complete tool for digital image forensics.
Highlights
Wide digital image usage has led to their intended manipulation
When two or more images are required in order to build a new image it is called image splicing.Imaging forensic techniques are applied to distinguish and classify authentic and manipulated images
The efficiency of the classifier is evaluated on the basis of accuracy, feature dimensionality, feature selection and computation time
Summary
Some manipulations can make the image more informative and useful These manipulation types are called image enhancements. Other manipulations could change the content of the image altogether. Such manipulations are termed as image forgeries [1]. When two or more images are required in order to build a new image it is called image splicing.Imaging forensic techniques are applied to distinguish and classify authentic and manipulated images. These techniques utilize the image data to locate whether the questioned image is authentic or manipulated. Some techniques extract features to check inconsistencies present in the image itself
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
