Message Insertion Using the Convolutional Neural Network Model Approach

Authors

  • Lita Ambarwati Universitas Prima Indonesia
  • Agrifa Darwanto Sirait Universitas Prima Indonesia
  • Bella Siska Tambun Universitas Prima Indonesia
  • Eko Paskah Jeremia Purwanto Universitas Prima Indonesia
  • Amir Mahmud Husein Universitas Prima Indonesia

DOI:

10.33395/sinkron.v4i1.10159

Keywords:

Convolutional Neural Network, Citra Digital, Deep Learning, Steganography.

Abstract

One problem in computer vision that has long been sought for a solution is the classification of objects in the image in general. How to duplicate the ability of humans to understand image information, so that computers can recognize objects in the image as humans do. The feature engineering process used is generally very limited where it can only apply to certain datasets without the ability to generalize to any type of image. That is because various differences between images include differences in perspective, differences in scale, differences in lighting conditions, deformation of objects, and so on. Academics who have long struggled with this issue. The application of the Convolutional Neural Network (CNN) method for the insertion of messages in an image with the aim of securing the proposed message produces good security, from the test results, it can be concluded as follows The Convolutional Neural Network (CNN) method requires computing time to insert messages in a secret image. The model framework uses 2 (two) images with the aim of the cover image as input and the secret image where the secret image has been inserted a message so that the secret is not visible. The cover image that has been inserted a secret picture that contains the message looks not much different, but the file size of the secret picture has increased by 66%.

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How to Cite

Ambarwati, L., Sirait, A. D., Tambun, B. S., Purwanto, E. P. J., & Husein, A. M. (2019). Message Insertion Using the Convolutional Neural Network Model Approach. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 4(1), 215-220. https://doi.org/10.33395/sinkron.v4i1.10159

Issue

Vol. 4 No. 1 (2019): SinkrOn Volume 4 Number 1, October 2019

Section

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