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

  • Author(s): Bunk, J
  • Bappy, JH
  • Mohammed, TM
  • Nataraj, L
  • Flenner, A
  • Manjunath, BS
  • Chandrasekaran, S
  • Roy-Chowdhury, AK
  • Peterson, L
  • IEEE
  • et al.
Abstract

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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