Revolutionizing Image Encryption: Data Hiding Model Based on Optimized Neural Network

Authors

  • Pallavi S. Chakole, Siva Rama Krishna T., Rahul Mishra, Beemkumar Nagappan, Sachin S. Pund, Jasneet Kaur

Keywords:

Data Hiding, Elliptic Curve Cryptography (ECC), Image Encryption, Information security Multi-rate Salient Gated Recurrent Neural Networks (MSG-RNN).

Abstract

Information security researchers are currently focusing on Revolutionizing Image Encryption in data hiding for safe digital data movement. Preserving information that has been hidden in a system is one of the core concepts of data hiding. For image encryption, data hiding is the process of embedding private data into images to ensure it is hidden from perceptions by other people. In this research, the hidden data is retrieved by our proposed Multi-rate Salient Gated Recurrent Neural Network (MSG-RNN) and it employs a dependent classification method to recover images from encrypted images. We gathered a data of various kinds of image data. Following the encryption of the original image, we established the Elliptic Curve Cryptography (ECC) method and created an innovative image encryption technique to improve security by data hiding. We calculated our proposed method's bit error rates. The comparison evaluation is performed with various methods to estimate proposed technique. Using the MSG-RNN approach on a number of images produced superior outcomes and they include the results for boats (0.0714), peppers (0.0712), baboons (0.0716) and airplanes (0.0719). The experimental outcomes offered that the proposed MSG-RNN technique performed better than other existing methods in data hiding process to enhance image encryption.

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Published

26.03.2024

How to Cite

Siva Rama Krishna T., Rahul Mishra, Beemkumar Nagappan, Sachin S. Pund, Jasneet Kaur, P. S. C. . (2024). Revolutionizing Image Encryption: Data Hiding Model Based on Optimized Neural Network. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1052–1057. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5504

Issue

Vol. 12 No. 21s (2024)

Section

Research Article

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