Digital Image Forgery Detection Using Deep Learning Models

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.