# 5D Gauss Map Perspective to Image Encryption with Transfer Learning Validation  
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1 Department of Electronics and Communication Engineering, Amity University MP, Gwalior 474005, India; [EMAIL_ADDRESS]  
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2 Department of Information Technology, National Institute of Technology, Raipur 492010, India; bharti.salunke@969 mgmail.com  
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3 Department of Electronics and Communication Engineering, National Institute of Technology, Warangal 506004, India; [EMAIL_ADDRESS]  
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4 Department of Computer Science Engineering, Amity University MP, Gwalior 474005, India; [EMAIL_ADDRESS]  
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5 Center for Quantitative Genetics and Genomics, Aarhus University, 8000 Aarhus, Denmark5 Center for Quantitative Genetics and Genomics, Aarhus University, 8000 Aarhus  
fewer parameters and variables, and their phase space trajectories are straightforward. Thus, chaotic signal estimating technologies can anticipate their beginning circumstances, characteristics, and trajectories. On the contrary, a high-dimensional (H.D.) chaotic map, particularly a Hyper chaotic map, does have more system parameters, a much more complicated structure, and more incredible chaotic performance. As a result, the H.D. chaotic map has the potential to be the ideal model for picture encryption [3]. Numerous chaotic maps are employed in image encryption; including the Logistic [4], Lorenz system [5], Arnold, Tent [6; 7], Baker [8], Henon [9], Piecewise [10], and Gauss map [11]. Furthermore, chaotic maps create random values used as secret keys in encryption. These maps aid in the encryption process by utilizing confusion and diffusion. Because of the recent pace in data science, chaotic cryptography based on artificial neural networks (ANNs) has been widely developed and also extensively researched due to the following attributes: high fault tolerance, associative memory, massive parallelism, and nonlinear computing [12]. These characteristics have a critical role in enhancing chaotic security. A discrete Hopfield network is also used to produce a nonlinear consecutive cipher generation that can construct a quasi-stochastic series to develop the standard image when given a small number of stochastic parameters as cipher codes. ANN models were also used to encrypt the data [13]. The Sparse Auto Encoder (SAE) model is a sort of ANN model that provides an unsupervised learning baseline technique. It also retrieves a significant set of feature parameters simultaneously required for batch visual cryptography [14]. They first provide a secure method for generating the chaotic system's initial value in the proposed method of double image encryption. They then use the plaintext associated control pointers, employed as the CNN's kernel to affect duplicate image scrambling [15]. Another method is a bit-level split-fusion technique. Two images are represented in binary form because the higher four-bit image has a substantial amount of image information. The lower four-bit contains a modest amount of information. The high four bits are combined to make a new picture; here, two images are merged, and the lower-four bits are used. At last, two encryption channels are used to encrypt the split pictures. The benefits of optical technology in parallelism and more significant computation of complex two-dimensional data are self-evident. The optical-based encryption is used for the portion of the split image that has to be encrypted quickly. In contrast, the digital image encryption network is used for the remaining part. A dynamic adaptive diffusion strategy is provided in this literature to ensure the security of the digital encryption channel. Experiments and performance assessments have revealed that the strategy outperforms the competition. The Gauss map is one of the chaos-based technique types that generate random numbers well. When coupled with substitution and permutation, it produces an encryption technique that is more resistant to hackers [16].