A Review On For Gery Detection In Social Media Images

Abstract

The increasing abuse of image editing software causes the authenticity of digital images questionable. The widespread availability of online social networks(OSNs) makes them the dominant channels for transmitting forged images to report fake news. The last decade has seen lot of research advancement in the area of digital image forensics, where the investigation for possible forgeries is based on post-processing of images. Deep learning approaches have shown promising results in various image classification problems but cannot find hidden patterns in digital images, which can reliably detect image forgeries. The objective of the proposed approach is to detection the accuracy. In addition to analyze the schemes and evaluate and compare their performances in terms of a proposed set of parameters, which may be used as a standard benchmark for evaluating the efficiency of any general copy-move forgery detection technique for digital images. We further incorporate the tailored noise into arobust training framework, significantly improving the robustness of the image forgery detector. The comparison results provided by them would help a user to select the most optimal forgery detection technique, depending on the author requirements. This paper discuses various forgery detection in social media images and suggests new idea of detection.