Enhancing Cross-Dataset Generalization in Image Forgery Detection via Mixed-Domain Training and Adaptive Thresholding

Abstract

Image forgery detection is an important part of digital forensics especially as more and more sophisticated image manipulating tools become more and more accessible. Despite the fact that deep learning models have shown high accuracy on benchmark datasets, their performance on heterogeneous datasets is a major challenge because of the domain shift and changes in manipulation methods. The paper suggests a hybrid training system and adaptive thresholding to improve the generalization of cross-datasets in image forgery detection. A model trained on CASIA dataset performed well on in-domain data but has a significant decrease when tested on unseen data with only a 48.5% accuracy on the COVERAGE dataset. To overcome this weakness, a mixed-domain learning approach that used samples across various datasets such as CASIA and COVERAGE was proposed in order to enhance feature robustness. The suggested solution was tested using three benchmark data sets, including CASIA, Columbia and COVERAGE. The experimental results show high accuracy of CASIA (approximately 95%), Columbia (approximately 95%), and considerably better accuracy on COVERAGE at 91.4%. The model also obtained high AUC-ROC value of 0.994 which implies that there was great class separability. Further analysis based on the precision, recall and F1-score validates balanced and consistent performance. Moreover, qualitative analysis in terms of score distribution and prediction visualization depicts obvious discrimination between genuine and manipulated photos. The results indicate that mixed-domain training is a practical approach that can reduce dataset bias and increase generalization, which is why the proposed framework can be adopted in the real-world forensic use. The work offers a complete solution to domain adaptation issue in the image forgery detection.