Resampling is an important signature of manipulated images. In this paper, wepropose two methods to detect and localize image manipulations based on acombination of resampling features and deep learning. In the first method, theRadon transform of resampling features are computed on overlapping imagepatches. Deep learning classifiers and a Gaussian conditional random fieldmodel are then used to create a heatmap. Tampered regions are located using aRandom Walker segmentation method. In the second method, resampling featurescomputed on overlapping image patches are passed through a Long short-termmemory (LSTM) based network for classification and localization. We compare theperformance of detection/localization of both these methods. Our experimentalresults show that both techniques are effective in detecting and localizingdigital image forgeries.
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