The definition for the artificial neural network given by one of the inventor of neural computer is given by,A computing system made up of the simple externally inters related processing elements, which practice information by their dynamic state response to their peripheral inputs. Then the Bayesian classification has to be done this will classify the forged object from the original image, which is based on the method of maximum likelihood.ДЇor each and every process preprocessing is the necessary one, here the preprocessing is done with the help of the artificial neural network. From this normalized matrix we can compute the certain features of the image. This preprocessed image is further converted into GLCM.Then we will get the normalized matrix of this image. In this the input image is preprocessed with the help of the neural network training tool. The entire process in this paper can be defined with the help of the flow chart given below. This training process will take the least amount of time compared to the other methodologies. The given input image is trained with the standard format of the image. Here the training is done for the image format. The process involving neural network training will train the input image according to our needs. Which are, format analysis of the image, error level analysis present in the image and the inconsistency in the image quality. This forgery detection method can be done in different forms according to the type of the forgery present in an image.The forgery detection which occurred in an image can be detected in three levels. As such type of the process needs an another factor of finding camera PRNU,which is varying for different cameras. In the previous works the classification is done with the Bayesian MRF approach for PRNU based image forgery detection. Finally the classification of the forged object is done by the Bayesian classifier. The second one is application of the glcm features. The initial step is the preprocessing which is done by the neural network training. This process can be classified into three major classification blocks. hence it is important to find the effective tool for the detection of the image forgery ,among that ,Bayesian classifier is one such tool for the prediction of the forgery in the digital image here the processing is done on the whole image instead of individual pixels. The image forgery means the manipulation of the digital images to conceal some meaningful or useful information, this may be done for also illegal message transaction The image processing techniques done on the images are wide in varieties which will cause the different effect on the images likewise some of the techniques such as copy- move (or) splicing, signature, watermarking or any other object which get added to the original by various means. Keywords-GLCM(Gray Level Co-occurrence Matrix), PRNU(Photo Response Non Uniformity),Bayesian classifier, Neural Network. The neural network training has to be done before the application of the GLCM features to predict whether the given input image is forged or not.Later,the processing on the image has to be done with the help of the Bayesian classifier to detect the forged part of the image.

In which image forgery detection methods based on the PRNU(photo response non uniformity) has the major disadvantage that forgery cannot be detected in the absence of camera PRNU.In this paper we are going to detect the forgeries in the image with the help of the GLCM(gray level co-occurrence matrix). Hence to get the authenticity of an image, we go for the image forgery detection methodologies. A Bayesian Classifier Approach for GLCM Based Image Forgery DetectionДЄ.V.C College of Engineering Tamilnadu, India.ДЄbstract-Generally the image manipulation techniques employed on the digital images causes the tampering on the image ,in which the noise will also get added to the image.

Glcm matlab 2012