A chaotic based image encryption scheme using elliptic curve cryptography and genetic algorithm

Abstract

In the era of digital communication and data security, image encryption plays a crucial role in safeguarding sensitive information. Protecting sensitive visual data from unauthorized access drives the pursuit of advanced image encryption methods. This paper proposes a novel approach to enhance image encryption by combining the power of a chaotic map, elliptic curve cryptography, and genetic algorithm. The chaotic map, specifically Arnold’s cat map, is employed to introduce chaos and randomness into the encryption process. The proposed image encryption process involves applying Arnold’s cat map for shuffling the pixel positions, followed by elliptic curve cryptography for encrypting the pixel values using public and private keys. Additionally, a genetic algorithm is employed to optimize the key generation process, enhancing the security of the encryption scheme. The combined utilization of these techniques aims to achieve a high level of confidentiality and robustness in image encryption. The algorithm underwent thorough analysis. It achieved a maximum entropy score of 7.99, indicating a high level of randomness and unpredictability in the encrypted data. Additionally, it exhibited near-zero correlation, which suggests strong resistance against statistical attacks. Moreover, the cryptographic range of possible keys was found to be $$2^{511}$$ 2 511 . This extensive key space makes the algorithm highly resilient against brute force attacks. It took only 0.5634 s to encrypt a moderately sized $$512\times 512$$ 512 × 512 pixel image with an 8-bit image on a standard desktop computer with a 2.3 GHz processor and 16 GB of RAM. The experimental findings confirm that the proposed approach is highly effective and efficient in safeguarding sensitive image data from unauthorized access and potential attacks. This scheme has the benefit of allowing us to protect our private image data while it’s being transmitted.

A two-dimensional chaotic model and its application in image encryption

“…From table 6 and table 7 we can see that our results are very close to the desired value 8. Although [20,23,24,26] is slightly higher than us in terms of information entropy value, we are relatively better than it in terms of UACI, correlation analysis and other indexes. To further test the randomness of the pixel value distribution of the encrypted image, we introduced the chisquare test, which passes the test when the value of the chi-square test is less than or equal to 293.2478.…”