FSM and RRU-net module for Image Splicing Forgery Detection
- RU-Net, residual propagation, residual feedback, spliced image
Nowadays, it can be difficult to tell whether an image is real or fake. Thanks to technological advancements, an image can be altered or falsified in a matter of seconds. Finding these forgeries has grown to be a major problem in the modern world. Although an image could be crucial evidence, it will be useless if it is faked. Methods for distinguishing between pictures that have been edited and those that have been computer-generated must be developed. In order to identify these forgeries, we plan to create an Image Forgery Detection Model that combines FSM and RRU-Net. Residual propagation and residual feedback are two distinct approaches that are combined in RRU-Net, which stands for Ringed Residual Structure and Network Architecture. To find long-distance dependencies, the Feature Similarity Module, or FSM, will be employed. Our suggested system combines FSM and RRU-Net to improve accuracy. We will extract the differences in the picture attributes between the modified and unmodified parts using image patches of different sizes. Once the forged area has been identified, the final region will be shown in color. The method will prove useful in the future for identifying different types of spliced image frauds that appear on different social media platforms.