Image Authentication and Restoration Using Block-Wise Variational Automatic Encoding and Generative Adversarial Networks

The Internet is a conduit for vast quantities of digital data, with the transmission images being especially prevalent due to widespread use social media. However, this popularity has led an increase in security concerns such as image tampering and forgery. As result, authentication become critical technology that cannot be overlooked. Recently, numerous researchers have focused on developing techniques using deep learning combat various attacks. Nevertheless, based typically classify only specific types attacks are unable accurately detect tampered or indicate precise location areas. paper introduces novel framework utilizes block-wise encoding through Variational Autoencoder Generative Adversarial Network models. Additionally, includes classification mechanism develop separate models different images. In training phase, first divided into blocks same size data. goal enable model judge authenticity by generate similar original blocks. verification input can trained model, locate exact position tampering, reconstruct ensure ownership.