Enhancing image data security using the APFB model

Ensuring the confidentiality of transmitting sensitive image data is paramount. Cryptography recreates a critical function in safeguarding information from potential risks and confirming the identity of authorised individuals, thereby addressing the growing demand for enhanced image security. This paper presents a novel AES-permuted Feistel Blowfish (APFB) model that aims to improve image data security cost-effectively and enhance data protection by incorporating AES and Blowfish algorithms. The proposed model's utility over existing approaches stems from its computational efficiency and speed. The proposed model's resilience and security against several attack modalities are validated through a comprehensive range of methods, including extensive experimentation, histogram analysis, PSNR, entropy, MSE, CC, computational time, and NIST statistical tests. The outcomes yielded a PSNR of 67.26, NPCR of 99.6354, and UACI of 33.412. Additionally, the applicability of the proposed model is validated by utilising a practical case analysis. The outcomes exhibit the relevance of the proposed model in real-world applications.