Libraries at University of Nebraska-Lincoln

 

Citation

Kadam, K.D., Ahirrao, S. N.,Dr. Ketan Kotecha (2020). Bibliometric Analysis of Passive Image Forgery Detection and Explainable AI. Library Philosophy and Practice, 1-29.

Abstract

Due to the arrival of social networking services such as Facebook and Instagram, there has been a vast increase in the volume of image data generated in the last decade. The use of image processing tools like GNU Gimp, Adobe Photoshop to create doctored images and videos is a major concern. These are the main sources of fake news and are often used in malevolent ways such as for mob incitement. Before a move can be taken based on a fake image, we should confirm its realness. This paper shows systematic mappings of existing literature for image forgery detection using deep learning and explainable AI. This uses the Scopus database for data analysis and various tools such as Sciencescape, Gephi, Tableau and VOS Viewer. The study discovered that the largest number of reviews on image forgery detection using deep learning and explainable AI had explored very recently. It was observed that USA universities/institutions are foremost in the research studies focusing on this research topic.

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.