The week's pick
Reseach Article
Enhancing Image Encryption using Histogram Analysis, Adjacent Pixel Autocorrelation Test in Chaos-based Framework
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 22 |
| Year of Publication: 2024 |
| Authors: O.E. Taylor, C.G. Igiri |
10.5120/ijca2024923653
|
O.E. Taylor, C.G. Igiri . Enhancing Image Encryption using Histogram Analysis, Adjacent Pixel Autocorrelation Test in Chaos-based Framework. International Journal of Computer Applications. 186, 22 ( May 2024), 9-18. DOI=10.5120/ijca2024923653
Abstract
This paper investigates the effectiveness and durability of encryption algorithms by utilising Histogram Analysis, Adjacent Pixel Autocorrelation Test (APAT), and Key Sensitivity Tests. The work displayed promising outcomes by combining chaos theory, Deoxyribonucleic Acid (DNA) sequence operations, and a redefined hash function. This approach effectively modified the spatial relationships between pixels, minimised information duplication, and ensured responsiveness to substantial changes. Chaos-based systems are vulnerable to predictability because they contain deterministic patterns. The utilising histogram analysis, adjacent pixel autocorrelation test, inside a chaos-based framework is to strengthen image encryption by addressing flaws and assuring strong protection against prospective attacks. The results indicate that the proposed approach effectively achieves strong encryption. Statistical measures show a significant reduction in correlation coefficients (0.85 to 0.05) and entropy values (7.2 to 6.1). Despite these changes, the visual quality remains high, and the encryption is resistant to different attacks. This ensures secure transmission of images in sensitive applications. Moreover, the research supports the idea of integrating image-specific attributes into hash functions in order to improve encryption methods.
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