# Encryption of 3D or Higher-Dimensional Images: A Review
## 4 Literature Review
In addition, a stereographic projection was implemented to map the points on the complex plane, together with chaotic permutations, chaotic circuits, and Latin cubes. The key space is equal to 2\({}^{310}\), showing that it is large enough to prevent brute force attacks. The method was also evaluated in terms of key sensitivity, plaintext sensitivity, and histogram analysis. [PERSON] and [PERSON] [44] proposed a 3D medical image encryption scheme based on a biometric key and cubic S-box. The first step of the proposed method is the generation of a pseudorandom sequence, followed by confusion, substitution, and finally diffusion, in which the diffusion algorithm applied is based on ECA and multiplication of finite fields. The applied algorithm is capable of increasing the sensitivity of the cryptosystem, hiding the statistical characteristics of the plaintext. Biometric keys are used to overcome the limitations of traditional methods, while the use of the cubic S-box is to increase the nonlinearity of the image cryptosystem. [PERSON] et al. [45] used convolutional neural networks (CNNs) to obtain the saliency of each elementary image (EI) originating from a 3D medical image with different viewpoints, and the region of interest (ROI) in each EI was extracted. Furthermore, two-dimensional (2D) Moore light field and automatic cell images (MCAs) were used, whose balanced 2D MCA rule was applied to encrypt the ROI of each EI. The elementary image array (EIA) can be reconstructed into a full-color, parallax 3D image and displayed to healthcare professionals via an LCD display, so it can be viewed from different angles, allowing for the design of diverse treatment plans. The simulations used showed results that made brute force attacks unfeasible, e.g., the size of the key space corresponded to 216; 777; 216. The time required to encrypt and decrypt the image was calculated through simulation experiments. It should be noted that when large images are used in encryption methods, the time consumption tends to increase [41]. [PERSON] et al. [37] performed their experiments on the following equipment: CPU Core i7-8700, Windows 10 system with 8 GB of RAM, and with an image of size \(960\times 720\). The time to run DFrFT encryption, obtained by the simulation, was 2.4790 s.  
[PERSON] and [PERSON] [34], despite not declaring the equipment used in their simulations, also emphasized the time spent on encryption and decryption, in which they used the S-box and Henon map encryption methods. The tests were carried out on slice 35 of a 3D medical image and a value of 0.012 s was obtained for encryption and decryption, separately. [PERSON] et al. [41] executed the proposed encryption scheme on the Windows 10 operating system with an Intel(R) Core(TM) i9-10850K CPU at 3.60 GHz and 32 GB of RAM, using the MATLAB software. The authors demonstrated the effectiveness of their proposed parallel encryption scheme in reducing the processing time compared to two other encryption schemes. Their simulations showed that the time consumption of the parallel encryption scheme for encrypting a large-sized EIA was much less than that of the RMD scheme, which was reduced by more than five times. The encryption time of the scheme proposed in [41] was 18.28 ms and that of the technique that uses DNA sequences and chaotic systems was 29.43 ms.  
[PERSON] et al. [46] proposed a light-field 3D encryption scheme based on monocular depth rendering. A convolution residual network (CRN) was used, in which it was possible to generate the corresponding depth map from a single RGB image, simplifying the capture process of the elementary image matrix (EIA) according to image mapping, as the real-time performance of light-field 3D encryption technology based on the integral image principle is restricted by the acquisition speed and EIA data. Furthermore, reversible state loop cellular automata (RSL-CA) were used to encrypt a single RGB image. [PERSON] et al.