Efficient Approach towards Detection and Identification of Copy Move and Image Splicing Forgeries Using Mask R-CNN with MobileNet V1

Abstract

With the technological advancements of the modern era, the easy availability of image editing tools has dramatically minimized the costs, expense, and expertise needed to exploit and perpetuate persuasive visual tampering. With the aid of reputable online platforms such as Facebook, Twitter, and Instagram, manipulated images are distributed worldwide. Users of online platforms may be unaware of the existence and spread of forged images. Such images have a significant impact on society and have the potential to mislead decision-making processes in areas like health care, sports, crime investigation, and so on. In addition, altered images can be used to propagate misleading information which interferes with democratic processes (e.g., elections and government legislation) and crisis situations (e.g., pandemics and natural disasters). Therefore, there is a pressing need for effective methods for the detection and identification of forgeries. Various techniques are currently employed for the identification and detection of these forgeries. Traditional techniques depend on handcrafted or shallow-learning features. In traditional techniques, selecting features from images can be a challenging task, as the researcher has to decide which features are important and which are not. Also, if the number of features to be extracted is quite large, feature extraction using these techniques can become time-consuming and tedious. Deep learning networks have recently shown remarkable performance in extracting complicated statistical characteristics from large input size data, and these techniques efficiently learn underlying hierarchical representations. However, the deep learning networks for handling these forgeries are expensive in terms of the high number of parameters, storage, and computational cost. This research work presents Mask R-CNN with MobileNet, a lightweight model, to detect and identify copy move and image splicing forgeries. We have performed a comparative analysis of the proposed work with ResNet-101 on seven different standard datasets. Our lightweight model outperforms on COVERAGE and MICCF2000 datasets for copy move and on COLUMBIA dataset for image splicing. This research work also provides a forged percentage score for a region in an image.

Enhancing Digital Image Forgery Detection Using Transfer Learning

“…The network employs a fully convolutional encoder-decoder architecture, and the Dilated Frequency Self-Attention Module (DFSAM) in the bridge layer adjusts fused features. [21] introduced a lightweight model using mask R-CNN with MobileNet to detect copy-move and image-splicing forgeries.…”

DMobile-ELA: Digital Image Forgery Detection via cascaded Atrous MobileNet and Error Level Analysis

“…An interesting approach was presented by Sudiatmika et al [5] , who utilized the idea of error-level analysis (ELA) in conjunction with CNNs to create a more universal tool for detecting various types of forgery. Sudiatmika proposed normalizing the images before pursuing ELA calculation and feeding the resulting images to a VGG16 network.…”

“…Image tampering and forgery include a wide range of types such as copy-move, splicing, retouching and image morphing [5]. Copy-move forgery includes copying a piece of the same picture and moving it to cover another part of the image, while splicing involves copying a part of an image to place it in another image.…”

Fully Automated Skull Stripping from Brain Magnetic Resonance Images Using Mask RCNN-Based Deep Learning Neural Networks

“…Mask–RCNN offers more precise and accurate results [ 40 ]; however, in some cases, it takes around 48 h to train the system. Numerous public databases such as Common Objects in Context (COCO) are available with training weights to train systems using the transfer learning approach [ 41 , 42 ]. Overall, Mask–RCNN is an effective tool for image analyses and has the potential for further advancements in computer vision.…”