Copy move forgery detection and segmentation using improved mask region-based convolution network (RCNN)

Copy-move forgery (CMF) is a common image manipulation approach that uses the information from the same sample to manipulate it with the intent of hiding the required content. Several approaches have been designed for the timely detection of CMF; however, accurate identification of manipulated samples is a complicated job due to the similar capturing conditions of the copied content as the patch is taken from the same image. Moreover, the occurrence of several post-processing attacks i.e., noise, blurring, brightness variations, etc. further enhances the difficulties of the detection approaches. In this work, we attempted to cover the limitations of existing methods by proposing a deep learning (DL)-based approach for the accurate detection of CMF. A custom Mask-RCNN model with the DenseNet-41 as the base network is presented which is capable of nominating a better set of image features and presents the complex image transformation effectively. More descriptively, the DenseNet-41 model is used as the base network for deep keypoints extraction which is then localized, segmented, and categorized by the Mask-RCNN model to locate the manipulated area. We have tested the proposed model on three standard databases namely the CoMoFoD, MICC-F2000, and CASIA-v2 databases, and attained a precision of 98.12%, 99.02%, and 83.41%, respectively. We have reported the results for numerous image post-processing attacks and confirmed that the presented work is robust to detect the CMF in the presence of translation, scale variations, rotation, color changes, noise, compression, and blurring in images. We have confirmed through extensive quantitative and qualitative evaluation that the DenseNet-41-based Mask-RCNN model is robust to CMF detection and can assist forensic analyzers to detect forensic manipulations accurately. • Presented a custom DenseNet-41-based Mask-RCNN model for features extraction which improved the accuracy to detect the manipulated content while minimizing both the training and testing time complexity. • Accurate detection of forged content due to the robustness of the DenseNet-41 framework. • Proficient localization and classification accuracy of the forged region in manipulated samples due to the ability of Mask-RCNN to tackle the multiple CMF cases and the over-fitted training data. • Performed extensive experimentation to show the reliability of the presented method in identifying single and multiple CMF cases from the digital images.