JPEG image tampering localization based on normalized gray level co-occurrence matrix

Object based Image Splicing Localization using Block Artificial Grids

“…But, the refining of the probability map in post-processing influences localization results. To overcome it, [23] used a mixture model based on normalized grey level co-occurrence matrix (NGLCM) and obtained more accurate localization with the prior knowledge of both tampered and original regions. To get this, they used conditional probabilities of tampered regions and original regions of DCT blocks in first, second, and thirdorder statistics.…”

“…We evaluated average TPR and FPR and F-measure for all the selected images from the CASIA dataset and compared them with [23] and [24] to analyze the performance of the proposed method.…”

“…The method of [23] is based on a normalized gray level co-occurrence matrix on 8x8 DCT coefficients and, using the Bayesian posterior probability map, localized the tampering objects. Whereas, the method [24] uses a deep learning method based on Fully Convolutional Networks (FCN) with Region Proposal Network (RPN) to localize the tampered region.…”

“…To evaluate the superiority of the proposed method, we compared our results with conventional and deep learning methods. Table I contains the Comparative results of the proposed method with [23] and [24] methods on both datasets based on average F-measure. FCN methods [24] prove to have superior performance than the conventional statistical-based methods [23].…”

“…Table I contains the Comparative results of the proposed method with [23] and [24] methods on both datasets based on average F-measure. FCN methods [24] prove to have superior performance than the conventional statistical-based methods [23]. From the results, it is evident that BAG noise on individual objects in the proposed method enables us to have much superior performance than [23].…”