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Detection and Localization of Image Forgeries using Resampling Features and Deep Learning
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Authors: Jason Bunk,Jawadul H. Bappy,Tajuddin Manhar Mohammed,Lakshmanan Nataraj,Arjuna Flenner,B. S. Manjunath,Shivkumar Chandrasekaran,Amit K. Roy-Chowdhury,Lawrence Peterson
ArXiv: 1707.00433
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1707.00433v1
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.
ArXiv: 1707.00433
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1707.00433v1
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.
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