A Hybrid MOPNA-SPM Algorithm for Secure Digital Information Embedding in Enterprise Data Protection

With the acceleration of enterprise digital transformation, the risk of sensitive data leakage has significantly increased. Traditional Digital Information Embedding (DIE) techniques suffer from critical limitations: limited embedding capacity, insufficient concealment (prone to visual distortion), weak robustness against attacks (e.g., noise and compression), and high computational complexity, failing to meet enterprises' demands for secure and large-scale sensitive data hiding. To address these issues, this study proposes a novel hybrid algorithm that integrates the Modulus Calculations on Prime Number Algorithm (MOPNA) and Single Pixel Modification (SPM) algorithm, which is an innovative combination that leverages MOPNA's pixel grouping strategy (dividing carriers into dual-pixel groups) and modular operation optimization based on prime weight, along with SPM's "high capacity-low distortion" single-pixel adjustment advantage. Based on this hybrid algorithm, a DIE model integrated with a Security and Authentication Module (SAM) is constructed to enhance data security during transmission and storage. The core contribution of this research lies in developing a DIE model with higher embedding capacity and stronger robustness than traditional methods. The practical application in an architectural design enterprise shows that the model achieves an embedding capacity of 3.51 bpp (36.58% higher than the SPM and 100.57% higher than the INFO algorithm), a Peak Signal-to-Noise Ratio (PSNR) of 48.25 dB, and a Structural Similarity (SSIM) of 0.98, ensuring near-lossless visual concealment. In terms of security, its anti-noise recovery rate reaches 95.50%, the anti-compression attack recovery rate is 90.25%, and the average information extraction time is only 0.45 s per image (62.50% faster than the SPM). This model provides a new technical solution for secure transmission and storage of enterprise sensitive information, with important reference value for data protection in other fields.