Integrating Quadratic Polynomial and Symbolic Chaotic Map-Based Feistel Network to Improve Image Encryption Performance

This research introduces an innovative image encryption method that amalgamates two secure and efficient chaotic maps, namely a 2D Simplified Quadratic Polynomial Map (2D-SQPM) and a 2D Symbolic Chaotic Map (2D-SCM), within an enhanced Feistel network structure. The primary motivation for this research is to address the limitations of current image encryption methods that are vulnerable to statistical and differential attacks. A hash function is also integrated to elevate the key’s security and sensitivity. Unlike standard Feistel networks, which split the plaintext into two parts and employ only XOR operations at the bit level, this research’s Feistel Network modification involves dividing the plaintext into four sections and introducing a diverse set of operations, including substitution and permutation at both the bit and byte levels across different parts, thereby optimizing confusion and diffusion effects. The empirical evaluation demonstrates that this method significantly reduces pixel correlation and strengthens encryption against statistical and differential attacks. Supported by various analytical tools like entropy analysis, NPCR, UACI, chi-square, key space and sensitivity analysis, robustness testing, and NIST suite evaluations, the proposed method significantly enhances image encryption performance. In conclusion, the proposed method effectively secures image data and sets a new benchmark in image encryption. The significance of this research lies in its integration of complex, chaotic dynamics and advanced encryption mechanisms, providing a substantial contribution to digital information security.