Message Insertion Using the Convolutional Neural Network Model Approach

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Lita Ambarwati Agrifa Darwanto Sirait Bella Siska Tambun Eko Paskah Jeremia Purwanto Amir Mahmud Husein
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Lita Ambarwati |

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Lita Ambarwati, Agrifa Darwanto Sirait, Bella Siska Tambun, Eko Paskah Jeremia Purwanto, Amir Mahmud Husein


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%.

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


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AMBARWATI, Lita et al. Message Insertion Using the Convolutional Neural Network Model Approach. SinkrOn, [S.l.], v. 4, n. 1, p. 215-220, oct. 2019. ISSN 2541-2019. Available at: <>. Date accessed: 13 july 2020. doi:
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