Open AccessDigital Image Forgery Detection Using Deep Learning Models
Publication year - 2022
Publication title -
international journal of emerging trends in engineering research
Language(s) - English
Resource type - Journals
ISSN - 2347-3983
DOI - 10.30534/ijeter/2022/091042022
Subject(s) - benchmark (surveying) , deep learning , artificial intelligence , computer science , random forest , image (mathematics) , feature (linguistics) , feature engineering , digital image , set (abstract data type) , feature extraction , machine learning , pattern recognition (psychology) , computer vision , image processing , linguistics , philosophy , geodesy , programming language , geography
One of the challenges to image trust in digital and online apps, as well as on social media, is the current situation. Image forgery detection is a technique for detecting and locating fabricated components in a modified image. A sufficient amount of features is necessary for good image forgery detection, which can be achieved using a deep learning model that does not require human feature engineering other handcraft feature techniques. In this paper we used the GoogleNet deep learning model to extract picture features and the Random Forest machine learning technique to determine whether or not the image was fabricated. The proposed approach is implemented on the publicly available benchmark dataset MICC-F220 with k-fold cross validation approach to split the data set in to training and testing dataset and also compared with the state-of-the-art approaches