3D image steganography using cellular automata transform and depth estimation network

Abstract

Traditional 3D image steganography is cumbersome and difficult owing to the special structure of 3D image, imposing a practical limit for its applications. Herein, we propose a 3D image steganography scheme utilizing cellular automata transformation (CAT). By using CAT with various rule numbers to hide images, the weakness of previous algorithms with only one transformation plane is overcome, and the hidden image quality can be improved by reducing energy loss. In the 3D reconstruction process, we apply a depth estimation algorithm based on a convolutional network to obtain corresponding depth maps from a single RGB image, simplifying the elemental image array (EIA) pickup process and even overcoming the phase difference problem caused by the traditional optical pickup process using lens arrays. Before hiding, we use the maximum length cellular automata (MLCA) to encrypt a single RGB image instead of encrypting EIA images with huge data, which greatly reduces the complexity of the encryption algorithm and improves efficiency. Finally, we reconstruct the decrypted image optically and obtain a full color and full parallax 3D image through the display device.