Convolution Neural Network with Dual Tree Complex Wavelet Transform Preprocessor for Universal Image Steganalysis

Authors

  • Halah Hasan Mahmoud Computer Center, University of Baghdad , Iraq
  • Hanaa Mohsin Ahmed Department of Computer Science , University of Technology-Iraq , Iraq

DOI:

https://doi.org/10.29304/jqcm.2019.11.3.606

Keywords:

Dual tree complex wavelet transform,, convolution neural network,, Accuracy,, Precision.

Abstract

Recently, deep learning models based on convolutional neural networks (CNN) have been used in image steganalysis problems. In this paper, we present different architecture of CNN with dual tree complex wavelet transform for preprocessing before input images put into system. The main task of this transform is for exploiting the difference between cover and stego images through shift variance property. The net consists of five successive convolutions layers. Each one following by normalization and pooling layers ends with fully connected layer. The performance of system is evaluated through accuracy, precision, recall and f-score measures. The results show effectiveness of it with more than 0.9 precision values. HUGO, WOW and UNIWARD algorithms selected for implementation.

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Published

2019-09-09

How to Cite

Mahmoud, H. H., & Ahmed, H. M. (2019). Convolution Neural Network with Dual Tree Complex Wavelet Transform Preprocessor for Universal Image Steganalysis. Journal of Al-Qadisiyah for Computer Science and Mathematics, 11(3), Comp Page 43–58. https://doi.org/10.29304/jqcm.2019.11.3.606

Issue

Section

Computer Articles