The influence of digital images on modern society is incredible, image processing has now become a significant component in almost all the areas. But storing images in a safe and sound way has become very complicated. Sometimes, for processing we can only use raster bitmap format. Therefore processing of such images should be carried out without knowledge of past processing on that image. Even though many image tampering detection techniques are available, the number of image forgeries is increasing. Therefore it is important to find the weaknesses of offered detection methods to prevent further forgeries. In this paper, a new approach is designed to prevent the bitmap compression history. Then it also explains how this can be used to perform unnoticeable forgeries on the bitmap images. It can be done by the estimation, examination and alteration in the transform coefficients of image. The existing methods for identification of bitmap compression history are JPEG detection and Quantizer estimation. The JPEG detection is used to find whether the image has been previously compressed. But the proposed method indicates that proper addition of noise to an image's transform coefficients can adequately eliminate quantization artifacts which act as indicators of JPEG compression. Using the proposed technique the modified image will appear to have never been compressed. Therefore this technique can be used to cover the history of operations performed on the image in the past and there by rendering several forms of image tampering.
Development of post-processing algorithms which cannot be detected by forensic tools is an active area of research in image processing. Median Filter (MF) is one among the denoising schemes which is specifically targeted by the forensic toolsbecause of its wide application in commercial raster graphic editors, simplicity, fast computation and detail preserving characteristics. Methodsbased on Convolutional Neural Networks (CNN) and Variational Deconvolution (VD), meant for reducing the forensic detectability of MF by removing the traces of filtering from the output images are computationally intense. A simple and computationally feasible approach for removing the traces of median filtering from the output images, thereby to reduce the forensic detectability of MF is proposed in this paper. In the proposed approach, blurred edges in the output of MF are restored with the help of Unsharp Masking (UM). Optimum value of the amount which controls the degree of sharpening in the UM algorithm is determined via minimum error sense criterion by making use of Peak Signal to Noise Ratio (PSNR) between input and processed images as objective function. Values of PSNR and Structural Similarity Index Metric (SSIM) between input and output images exhibited by the proposed algorithm are found to be higher than those exhibited by methods based on CNN, VD and combined framework of VD and Total Variation (TV) minimisation.
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