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

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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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: <https://jurnal.polgan.ac.id/index.php/sinkron/article/view/10159>. Date accessed: 13 nov. 2019. doi: https://doi.org/10.33395/sinkron.v4i1.10159.
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References

Babaheidarian, Parisa, and Mark Wallace. Decode and Transfer: A New Steganalysis Technique via Conditional Generative Adversarial Networks. 2019, http://arxiv.org/abs/1901.09746.
Jamie Hayes and George Danezis “Generating Steganographic images via adversarial training 2017”.
Zhang, Zhuo, et al. “Generative Steganography by Sampling.” IEEE Access, 2019, pp. 1–1, doi:10.1109/access.2019.2920313.
S. Tan and B. Li, “Stacked convolutional auto- encoders for steganalysis of digital images," Asia-Pacific Signal and Information Processing Association, 2014 Annual Summit and Conference (APSIPA), 2014, pp.1-4.

G. Xu, H. Z. Wu, and Y. Q. Shi, “Structural design of convolutional neural networks for steganalysis," IEEE Signal Processing Letters, 23(5):708712, 2016

S. Wu, S. Zhong, and Y. Liu, “Deep residual learning for image steganalysis," Multimedia Tools and Applications, pp. 1-17, 2017.

G. Xu, “Deep convolutional neural network to detect J-UNIWARD," arXiv:1704.08378, 2017.

Vojtech Holub and Jessica J. Fridrich “Designing steganographic distortion using directional filters”. In WIFS, 2012.

Vojtech Holub, Jessica Fridrich, and Tomáš Denemark “Universal distortion function for steganogra- phy in an arbitrary domain” EURASIP Journal on Information Security, 2014(1):1–13, 2014.

Sari, Janer Irma, et al. “Implementasi Penyembunyian Pesan Pada Citra Digital Dengan Menggabungkan Algoritma HILL Cipher Dan Metode Least Significant BIT (LSB).” Jurnal Mantik Penusa, vol. 1, no. 2, 2017, pp. 1–8, http://e-jurnal.pelitanusantara.ac.id/index.php/mantik/article/view/253/156.

Abdul Kadir dan Adhi Susanto, “Teori dan Aplikasi Pengolahan Citra.” Andi Yogyakarta, Yogyakarta, p.2, 2013.


Tomáš Pevny, Patrick Bas, and Jessica Fridrich “Steganalysis by subtractive pixel adjacency matrix” ` information Forensics and Security, IEEE Transactions on, 5(2):215–224, 2010.

Tomas Pevny, Tomas Filler, and Patrick Bas “Using High-Dimensional Image Models to Perform Highly Undetectable Steganography” In Information Hiding, pp. 2010, Calgary, Canada, June 2010. URL https://hal.archives-ouvertes.fr/hal-00541353.

Yinlong Qian, Jing Dong, Wei Wang, and Tieniu Tan “Deep learning for steganalysis via convolutional neural networks” In SPIE/IS&T Electronic Imaging, pp. 94090J–94090J. International Society for Optics and Photonics, 2015b.

Lionel Pibre, Pasquet Jérôme, Dino Ienco, and Marc Chaumont “Deep learning for steganalysis is better than a rich model with an ensemble classifier, and is natively robust to the cover sourcemis match” arXiv preprint arXiv:1511.04855, 2015.

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