Context: There has been a rise in the usage of digital images these days. Digital images are being used in many areas like in medicine, wars, etc. As the images are being used to make many important decisions, it is necessary to know if the images used are clean or forged. In this thesis, we have considered the area of splicing forgery. In this thesis, we are also considering and analyzing the impact of low-resolution images on the considered algorithms. Objectives. Through this thesis, we try to improve the detection rate of splicing forgery detection. We also examine how the examined splicing forgery detection algorithm works on low-resolution images and considered classification algorithms (classifiers). Methods: The research methods used in this research are Implementation and Experimentation. Implementation was used to answer the first research question i.e., to improve the detection rate in splicing forgery. Experimentation was used to answer the second research question. The results of the experiment were analyzed using statistical analysis to find out how the examined algorithm works on different image resolutions and on the considered classifiers. Results: One-tailed Wilcoxon signed rank test was conducted to compare which algorithm performs better, the T+ value obtained was less than To so the null hypothesis was rejected and the alternative hypothesis which states that Algorithm 2 (our enhanced version of the algorithm) performs better than Algorithm 1 (original algorithm), is accepted. Experiments were conducted and the accuracy of the algorithms in different cases were noted, ROC curves were plotted to obtain the AUC parameter. The accuracy, AUC parameters were used to determine the performance of the algorithms. Conclusions: After the results were analyzed using statistical analysis, we came to the conclusion that Algorithm 2 performs better than Algorithm 1 in detecting the forged images. It was also observed that Algorithm 1 improves its performance on low-resolution images when trained on original images and tested on images of different resolutions but, in the case of Algorithm 2, its performance is improved when trained and tested on images of the same resolution. There was not much variance in the performance of both of the algorithms on images of different resolution. Coming to the classifiers, Algorithm 1 improves its performance on linear SVM whereas Algorithm 2 improves its performance when using the simple tree classifier.

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.