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Article

Enhancing Visual Data Security: A Novel FSM-Based Image Encryption and Decryption Methodology

1
Department of Information Security, Faculty of Information Technologies, L.N. Gumilyov Eurasian National University, Astana 010008, Kazakhstan
2
Department of Artificial Intelligence Technologies, Faculty of Information Technologies, L.N. Gumilyov Eurasian National University, Astana 010008, Kazakhstan
3
Higher School of Information Technology and Engineering, Astana International University, Astana 010008, Kazakhstan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(11), 4341; https://doi.org/10.3390/app14114341
Submission received: 18 April 2024 / Revised: 15 May 2024 / Accepted: 17 May 2024 / Published: 21 May 2024
(This article belongs to the Section Computing and Artificial Intelligence)

Abstract

The paper presents a comprehensive exploration of a novel image encryption and decryption methodology, leveraging finite state machines (FSM) for the secure transformation of visual data. The study meticulously evaluates the effectiveness of the proposed encryption algorithm using a diverse image dataset. The encryption algorithm demonstrates high proficiency in obfuscating the original content of images, producing cipher images that resemble noise, thereby substantiating the encryption’s effectiveness. The robustness of the proposed methodology is further evidenced by its performance in the National Institute of Standards and Technology Statistical Test Suite (NIST STS). Such achievements highlight the algorithm’s capability to maintain the stochastic integrity of encrypted data, a critical aspect of data security and confidentiality. Histogram analysis revealed that the encryption process achieves a uniform distribution of pixel values across the encrypted images, masking any identifiable patterns and enhancing the security level. Correlation analysis corroborated the success of the encryption technique, showing a substantial reduction in the correlation among adjacent pixel values, thereby disrupting spatial relationships essential for deterring unauthorized data analysis. This improvement indicates the algorithm’s efficiency in altering pixel patterns to secure image data. Additionally, a comparative analysis of correlation coefficients using various encryption methods on the Lenna image offered insights into the relative effectiveness of different techniques, emphasizing the importance of method selection based on specific security requirements and data characteristics.
Keywords: finite state machines; invertible finite automata; image encryption and decryption; NIST statistical test suite; histogram analysis; correlation analysis finite state machines; invertible finite automata; image encryption and decryption; NIST statistical test suite; histogram analysis; correlation analysis

Share and Cite

MDPI and ACS Style

Shakhmetova, G.; Barlybayev, A.; Saukhanova, Z.; Sharipbay, A.; Raykul, S.; Khassenov, A. Enhancing Visual Data Security: A Novel FSM-Based Image Encryption and Decryption Methodology. Appl. Sci. 2024, 14, 4341. https://doi.org/10.3390/app14114341

AMA Style

Shakhmetova G, Barlybayev A, Saukhanova Z, Sharipbay A, Raykul S, Khassenov A. Enhancing Visual Data Security: A Novel FSM-Based Image Encryption and Decryption Methodology. Applied Sciences. 2024; 14(11):4341. https://doi.org/10.3390/app14114341

Chicago/Turabian Style

Shakhmetova, Gulmira, Alibek Barlybayev, Zhanat Saukhanova, Altynbek Sharipbay, Sayat Raykul, and Altay Khassenov. 2024. "Enhancing Visual Data Security: A Novel FSM-Based Image Encryption and Decryption Methodology" Applied Sciences 14, no. 11: 4341. https://doi.org/10.3390/app14114341

APA Style

Shakhmetova, G., Barlybayev, A., Saukhanova, Z., Sharipbay, A., Raykul, S., & Khassenov, A. (2024). Enhancing Visual Data Security: A Novel FSM-Based Image Encryption and Decryption Methodology. Applied Sciences, 14(11), 4341. https://doi.org/10.3390/app14114341

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