A Systematic Approach to Detect Spliced and Forged Images using Deep Learning Technique

Abstract

The widespread accessibility of image editing tools has made it simpler to alter the contents of digital figures as multimedia technology has advanced. Moreover, photographs are more susceptible to counterfeiting when they are distributed through an open channel using information and communication technology (ICT). Due to the flaws in the telecommunications infrastructure, it is possible for hackers to make subtle but deceptive alterations to picture databases. If edited with a malicious intent, the phoney photographs might create serious social and legal problems. The use of sophisticated tools built through deep learning techniques can accurately detect changes to the mathematical image is required for the discovery of image forgeries. The sensitivity in photos is typically concealed through splicing forgeries. Extreme contrast is introduced by splicing in the edges, smooth areas, and corners. In-depth Learning