Enhancing the JPEG Ghost Algorithm using Machine Learning

Abstract: Background: With the boom in the internet space and social media platforms, a large number of images are being shared. With this rise and advancements in technology, many image editing tools have made their way to giving rise to digital image manipulation. Being able to differentiate a forged image is vital to avoid misinformation or misrepresentation. This study focuses on the splicing image forgery to localizes the forged region in the tampered image. Objectives: The main purpose of the thesis is to extend the capability of the JPEG Ghost model by localizing the tampering in the image. This is done by analyzing the difference curves formed by compressions in the tampered image, and thereafter comparing the performance of the models. Methods: The study is carried out by two research methods; one being a Literature Review, whose main goal is gaining insights on the existing studies in terms of the approaches and techniques followed; and the second being Experiment; whose main goal is to improve the JPEG ghost algorithm by localizing the forged area in a tampered image and to compare three machine learning models based on the performance metrics. The machine learning models that are compared are Random Forest, XGBoost, and Support Vector Machine. Results: The performance of the above-mentioned models has been compared with each other on the same dataset. Results from the experiment showed that XGBoost had the best overall performance over other models with the Jaccard Index value of 79.8%. Conclusions: The research revolves around localization of the forged region in a tampered image using the concept of JPEG ghosts. This is We have concluded that the performance of XGBoost model is the best, followed by Random Forest and then Support Vector Machine.