Secure 3D data hiding through cryptographic steganalysis resistance: reducing geometric inconsistency vulnerabilities

The advent of the metaverse has generated considerable interest in 3D models, although data transfer security continues to be a paramount issue. In the contemporary digital landscape, characterized by ubiquitous internet connectivity and widespread image distribution, the protection of sensitive data within 3D models is becoming increasingly imperative. Protecting private and sensitive information within 3D models has become essential in the current interconnected digital environment, which is marked by pervasive internet access and extensive model sharing. Existing transmission mechanisms are vulnerable to various cyber risks during the transfer of important 3D models via insecure networks. To address the challenges in securing sensitive information embedded in 3D models, this article introduces a contemporary and effective system that combines cryptography with 3D steganography techniques. This study employed AES-128 with cipher block chaining (CBC-IV) and an initialization vector to convert plaintext into ciphertext. The study employed SHA-256, salt, and a 32-bit password to produce the encryption key, creating a fundamental layer of protection. This research used encrypted data within a 3D facial model employing geometric characteristics. This study defined key regions, identified significant vertices, and assessed the importance of each vertex based on geometric characteristics. The present study included data on vertices adjacent to landmarks, which were rounded and augmented using an enlarged scale factor, resulting in a stego 3D model. The performance measurements show how well our method works, with a Peak Signal-to-Noise Ratio (PSNR) of 61.31 dB, a Mean Square Error (MSE) of 3.17, a correlation coefficient of 0.95, and a Region Hausdorff Distance (RHD) of 0.04. Our method attained Number of Pixel Change Rate (NPCR) and Unified Average Changing Intensity (UACI) values of 94.82 and 28.31, respectively, surpassing current methodologies. Our methodology addresses geometric inconsistency issues and adeptly conceals the model’s deformed geometry. In the future, we will investigate blockchain technology alongside 3D model encryption to enhance the security, authenticity, and transparency of safeguarded 3D model data.