Engineering, Technology & Applied Science Research Vol. 8, No. 1, 2018, 2555-2561 2555 www.etasr.com Gupta and Mohan: Color Channel Characteristics (CCC) for Efficient Digital Image Forensics Color Channel Characteristics (CCC) for Efficient Digital Image Forensics Surbhi Gupta Computer Science & Engineering Faculty I. K. Gujral Punjab Technical University Kapurthala, India royal_surbhi@yahoo.com Neeraj Mohan Computer Science & Engineering Faculty I. K. Gujral Punjab Technical University Kapurthala, India erneerajmohan@gmail.com 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. Keywords-image tampering; noise discrepancies; feature extraction; edge textural information; statistical evaluation I. INTRODUCTION Wide digital image usage has led to their intended manipulation. 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]. Image forgery is usually achieved using copy-move or image splicing operations [2-3]. When a subpart of an image is cropped, processed and then pasted into the original image the manipulation is called copy-move. 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 of these techniques try to define the origin of the image, whereas others try to compare it with some reference images. Some techniques extract features to check inconsistencies present in the image itself. An image may be expressed using different color models like RGB, YCbCr, L*a*b etc. Each color model has three channels. The image intensity information is spread in these three channels. The color information distribution among channels depends on the model used. In RGB Model, Red, Green and Blue channels contain equal amount of intensity information. But the human vision system cannot utilize RGB color for its apprehension. Luminance or brightness of color is the most important information which human vision system uses for understanding colors. Other models like L*a*b (Luminance, red minus green, green minus blue) and YCbCr (Luminance, chrominance blue, chrominance red) are based on Luminance. In these models, most of the intensity information is present in luminance channel. Another popular color model is HSV (Hue, Saturation and Value) model which is based on the human color perspective. It has hue, saturation and value channels for the distribution of intensity data. Different channels in these examples are capable to illustrate and bring out different features of an image. Color channel characteristics (CCC) for various models could be a genuine aid inimage analyzing. Various image forensic techniques have been proposed and analyzed in the last two decades. The author in [4] proposed a blind, noise based, passive image quality assessment model to transform the heuristics into noise structures. Mathematical models were developed to measure the edge sharpness, the random noise and the structural noise in an image objectively, without any reference image. Authors in [5] proposed Image Quality Measures (IQM) to expose compression and steganography using image statistical analysis. A linear regression classifier was used for image classification of original and manipulated images. Sequential floating forward search (SFFS) algorithm was applied for feature selection. The achieved accuracy was 91% and 80% for large and small manipulated regions of the image, respectively. Further, authors in [6] used noise variance of overlapping blocks to estimate manipulation in images, but this method did not execute well when images with high quality factors were spliced and resaved at a lower quality. Binary similarity measures (BSM) were introduced in [7]. BSM are based on correlation and texture features of the image. Feature selection was applied to attain an accuracy of 90% using joint feature sets. The major limitation of the approach was that its accuracy decreased when the image manipulated region was small. Authors in [8] proposed statistical noise feature extraction