Enhancing image security through nonlinear preprocessing and double random phase encoding using fractional fourier transform

Abstract

Image encryption is crucial for secure data transmission in fields such as IoT, medical imaging, and biometrics. This paper proposes an enhanced encryption framework that combines nonlinear preprocessing with double random phase encoding (DRPE) using the fractional fourier transform (FrFT). The diffusion process replaces the conventional XOR operation with a nonlinear hyperbolic tangent (tanh) function, improving confusion diffusion complexity and resistance to cryptanalytic attacks. Experimental results show a reduction in peak signal-to-noise ratio (PSNR) from 9.03 dB to 7.25 dB and a mean squared error (MSE) increase to 10×10³, indicating stronger encryption and lower correlation with the original image. The proposed method also enhances robustness against histogram and key sensitivity attacks. Statistical analyses, including entropy and number of pixels change rate (NPCR) metrics, demonstrate that the approach outperforms conventional DRPE methods while maintaining computational efficiency. This hybrid nonlinear and FrFT-based framework provides a practical and scalable solution for secure image transmission in sensitive and real-time applications.