Adaptive Image Encryption Using Henon Map Scrambling and Perona-Malik Diffusion
K Suriyagugan, S. Saradha · 2025
As digital data exchange continues to grow exponentially, ensuring the security of image data against cyber threats has become a pressing challenge. Conventional encryption schemes often suffer from inefficiencies in handling highdimensional image data, leading to vulnerabilities against cryptographic attacks. This paper presents a novel adaptive image encryption approach that leverages Henon Map scrambling and Perona-Malik anisotropic diffusion to achieve superior security, high randomness, and enhanced resistance to statistical and differential attacks. The Henon Map introduces a highly nonlinear chaotic transformation for pixel scrambling, effectively eliminating spatial correlation, while the Perona-Malik diffusion process spreads pixel intensity variations in an edge-preserving manner, further strengthening encryption robustness. Experimental evaluations on benchmark images confirm the efficacy of the proposed encryption scheme. The encrypted images achieve near-ideal entropy values (7.9991 bits), demonstrating a highly uniform intensity distribution. The correlation coefficient is significantly reduced to 0.0007, ensuring minimal pixel dependency. The Number of Pixel Change Rate (NPCR) reaches 99.63%, and the Unified Average Changing Intensity (UACI) achieves 33.21%, indicating strong resistance to differential attacks. Additionally, the encryption scheme exhibits a Peak Signal-to-Noise Ratio (PSNR) of 8.72 dB, ensuring a high level of distortion in the encrypted image. Computational efficiency is maintained with an average execution time of 0.395 sec, making the proposed scheme suitable for real-time applications such as IoT, medical imaging security, and cloud-based image storage.