Detection and Localization of Image Forgeries Using Resampling Features and Deep Learning

Jason Bunk; Jawadul H. Bappy; Tajuddin Manhar Mohammed; Lakshmanan Nataraj; Arjuna Flenner; B.S. Manjunath; Shivkumar Chandrasekaran; Amit K. Roy-Chowdhury; Lawrence Peterson; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 69-77

Resampling is an important signature of manipulated images. In this paper, we propose two methods to detect and localize image manipulations based on a combination of resampling features and deep learning. In the first method, the Radon transform of resampling features are computed on overlapping image patches. Deep learning classifiers and a Gaussian conditional random field model are then used to create a heatmap. Tampered regions are located using a Random Walker segmentation method. In the second method, resampling features computed on overlapping image patches are passed through a Long short-term memory (LSTM) based network for classification and localization. We compare the performance of detection/localization of both these methods. Our experimental results show that both techniques are effective in detecting and localizing digital image forgeries.