A Comprehensive Survey on Multiple Secret Image Recovery Techniques Using Advanced Visual Cryptography Models

Sensitive images may be safely and effectively protected using visual cryptography (VC), which divides them into several shares that must be reconstructed using a certain number of shares. With the introduction of several techniques including (k, n)-VC, Recursive Visual Cryptography (RVC), Extended Visual Cryptography (EVC), and Progressive Visual Cryptography (PVC), recent developments have expanded classic VC to facilitate the recovery of numerous secret images. By enabling the safe encryption and decryption of several mysteries inside a single set of shares, these approaches improve efficiency and security. Furthermore, methods like Halftone Visual Cryptography (HVC), Random Grid-Based Visual Cryptography (RGVC), and Color Visual Cryptography (CVC) have enhanced the quality of rebuilt images while preserving brightness and reducing pixel expansion. Hybrid approaches integrating cryptographic algorithms and machine learning techniques have been explored to optimize secret image recovery accuracy and security. This survey comprehensively reviews these methodologies, compares their performance metrics, and highlights the challenges in balancing contrast, computational efficiency, and multi-secret recovery accuracy. The paper also explores future research directions such as leveraging deep learning models and quantum visual cryptography for improved security and scalability in multiple secret image recovery systems.