PVD Method and Modulus Function-Based PVD Method for Image Steganography

I have developed MATLAB implementations of both the traditional Pixel Value Differencing (PVD) method and an enhanced Modulus Function-based PVD approach for image steganography research. In my implementation, the PVD method operates by analyzing adjacent pixel differences in cover images and embedding secret information into these differential values through dynamic range partitioning. The algorithm calculates pixel value variations between neighboring pixels and utilizes quantization ranges to determine optimal embedding capacities. My enhanced approach incorporates a modulus function modification that embeds secret data within the modulus operations of pixel values. This method employs modular arithmetic to distribute the payload more evenly across the image, improving steganographic performance by reducing detectable artifacts. The code implementation includes functions for calculating optimal modulus parameters, embedding data while maintaining statistical properties, and extracting hidden information with minimal distortion. Through comprehensive testing and analysis, I've found that both methods demonstrate significant potential for secure information transmission applications. The PVD method provides efficient embedding capacity while maintaining visual quality, whereas the modulus-based extension offers enhanced security against steganalysis through improved payload distribution. These techniques show promising applications for protecting sensitive data in digital communication systems, with particular effectiveness in maintaining image integrity while embedding substantial payloads.