Next-generation financial encryption using image analyzer algorithms: A design and implementation approach

The Image Analyzer Encryption Algorithm offers a novel approach to securing financial data by leveraging image-based encryption techniques alongside traditional cryptographic methods, such as AES. This research explores the design and implementation of the algorithm, which converts structured financial data into encrypted images using chaotic encryption and fractal analysis. The algorithm's performance was tested in a simulated financial environment, comparing it against traditional methods like RSA and AES. Results showed that the Image Analyzer demonstrated strong resistance to brute-force and quantum-based attacks, achieving a 90% success rate against quantum algorithms such as Grover’s and Shor’s. Although the algorithm introduced a modest increase in computational overhead, it remained efficient enough for real-time financial applications, offering significant scalability and flexibility across various data types. In addition, the algorithm’s parallel processing capabilities further optimized its performance, reducing bottlenecks during key generation and data transformation stages. This encryption model represents a next-generation solution for financial security, addressing vulnerabilities posed by the advent of quantum computing and increasingly sophisticated cryptanalytic techniques. Its ability to provide robust, quantum-resistant encryption makes it a critical tool for safeguarding financial transactions in the digital age.