Deep Learning-based Copy-Move and Spliced Image Forgery Detection

Abstract

This paper proposes a Deep Learning (DL) based pre-trained AlexNet model for detecting and localizing copy-move and spliced forgery in photos. To localise forgeries in a photo, a binary mask is constructed using sobel operators. Further feature vectors are extracted patch wise from the input pictures. The Spatial Rich Model (SRM) is employed to address the generalisation issues in the DL model. There are three datasets used: Columbia Uncompressed Image Splicing Detection Evaluation (CUISDE), CASIA 1, and CASIA 2. The overall performance of the model has a 98.59 percent total accuracy as against 98.176% reported in the existing literature.