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Integrity verification or forgery detection of an image is a difficult procedure, since the forgeries use various transformations to create an altered image. Pixel mapping transforms, such as contrast enhancement, histogram equalisation, gamma correction and so on, are the most popular methods to improve the objective property of an altered image. In addition, fabricators add Gaussian noise to the altered image in order to remove the statistical traces produced because of pixel mapping transforms. A new method is introduced to detect and classify four various categories including original, contrast modified, histogram-equalised and noisy images. In the proposed method, the absolute value of the first 36 Zernike moments of the pixel-pair histogram and its binary form for each image in the polar coordinates are calculated, and then those features that yield the maximum between-class separation, are selected. Some other features obtained from Fourier transform are also utilised for more separation. Finally, support vector machine classifier is used to classify the input image into four categories. The experimental results show that the proposed method achieves high classification rate and considerably outperforms the previously presented methods.