Detection of Copy-Move Forgery in Digital Image Using Multi-scale, Multi-stage Deep Learning Model

TLDR: A deep learning CNN model is developed using multi-scale input with multiple stages of convolutional layers and a sigmoid activation function is used to classify pixels into forged or non-forged using the final feature map.

Abstract: Images are an important source of information and copy-move forgery (CMF) is one of the vicious forgery attacks. Its objective is to conceal sensitive information from the image. Hence, authentication of an image from human eyes become arduous. Reported techniques in literature for detection of CMF are suffering from the limitations of geometric transformations of forged region and computation cost. In this paper, a deep learning CNN model is developed using multi-scale input with multiple stages of convolutional layers. These layers are divided into two blocks i.e. encode and decoder. In encoder block, extracted feature maps from convolutional layers of multiple stages are combined and down sampled. Similarly, in decoder block extracted feature maps are combined and up sampled. A sigmoid activation function is used to classify pixels into forged or non-forged using the final feature map. To validate the model two different publicly available datasets are used. The performance of the proposed model is compared with state-of-the-art methods which show that the presented data-driven approach is better.