Authors: Shubham Ballal, P.R Sonawane, Utkarsh,, Aashutosh Rawat, Deewan Singh
Abstract: In modern world, images are among the most significant sources of shared information.They include important information that even those who are illiterate can understand. The growing availability of advanced image editing tools has made detecting image forgeries a crucial problem in digital forensics.However, the majority of Forgery detection methods are limited to identifying a single kind of forgery, like image splicing or copy-move, which are not used in everyday life. In order to improve digital image forgery detection, this paper suggests a deep learning technique that combines CNN and ELA to simultaneously detect two types of image forgeries. The suggested method depends on determining the forged area’s com- pressed quality, which typically varies from the image’s overall compressed quality.The matrix subtraction of the original image compressed image is used as input to CNN model for training and detection. This research paper fine-tunes the CNN and uses robust compression levels in ELA to minimise complexity and maximise accuracy.
DOI: http://doi.org/
