ABSTRACT
The most common phenomenon in anti-forensic approach is to conceal image tampering artifacts through recapturing. Hence, forensic algorithms fail to detect tampering in manipulated images. The easy way to produce a Recaptured image to cover the manipulations is capturing it from LCD screens. Despite hiding manipulations, Recaptured images itself contain some characteristics that can be exploited by various algorithms to identify Recaptured images. High-resolution images contain sucient information to analyze multiple characteristics, but identication of low-resolution Screen-Captured images is problematic. Since low-resolution images have enough information to extract valid features. In this paper, a Convolutional Neural Network (CNN) is proposed that not only able to classify high-resolution images but can also distinguish low-resolution images as Single-Captured or Screen-Captured nely. The network is trained and validated with dierent images sizes and performed very well by achieving high accuracy. Experimental results show that, achieved accuracy of HLReCapNet surpassed other existing network and benchmarked machine learning algorithms.
