Deep Learning-Powered Approach for Image Forgery Detection
Mukesh Kumar, Abhishek Negi, Aditya Panwar, Rajat Mehar, Rohit Rawat, Teekam Singh · 2024
Photograph alteration has become popular due to the readily available tools for doing so. Because photos that have been manipulated can confuse many people by tricking the eye, moving and spreading across multiple platforms, and becoming material for rumors. This led researchers to develop further techniques for precisely identifying phony images. These days, the majority of conventional methods for determining whether a picture has been altered depend on taking out fundamental characteristics designed for a certain kind of fake. In fact, studies have demonstrated that neural network-based image fraud detection is incredibly effective. Neural networks actually do significantly better than other techniques when it comes to deciphering intricate hidden information from images. Deep learning models automatically generate the essential features as they go, in contrast to previous methods for spotting forgeries that required specific traits to be retrieved. In this document, we have examined traditional techniques for identifying image forgery and explored the CNN approach for forgery detection, widely acknowledged as a superior method in detecting image forgery.