  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.
