5D Gauss Map Perspective to Image Encryption with Transfer Learning Validation

Applied Sciences

卷:12

Neeraj Dhanraj Bokde¹  Venkatadri Marriboyina²  Sharad Salunke³  Mohammad Farukh Hashmi⁴  Bharti Ahuja⁵ 

[1] Center for Quantitative Genetics and Genomics, Aarhus University, 8000 Aarhus, Denmark;

[2] Department of Computer Science Engineering, Amity University MP, Gwalior 474005, India;

[3] Department of Electronics and Communication Engineering, Amity University MP, Gwalior 474005, India;

[4] Department of Electronics and Communication Engineering, National Institute of Technology, Warangal 506004, India;

[5] Department of Information Technology, National Institute of Technology, Raipur 492010, India;

关键词: chaotic map;    Gauss map;    image encryption;    pretrained models;    transfer learning;   

DOI  :  10.3390/app12115321

来源: DOAJ

【 摘 要 】

Encryption of visual data is a requirement of the modern day. This is obvious and greatly required due to widespread use of digital communication mediums, their wide range of applications, and phishing activities. Chaos approaches have been shown to be extremely effective among many encryption methods. However, low-dimensional chaotic schemes are characterized by restricted system components and fundamental structures. As a result, chaotic signal estimation algorithms may be utilized to anticipate system properties and their initial values to breach the security. High-dimensional chaotic maps on the other hand, have exceptional chaotic behavior and complex structure because of increased number of system parameters. Therefore, to overcome the shortcomings of the lower order chaotic map, this paper proposes a 5D Gauss Map for image encryption for the first time. The work presented here is an expansion of the Gauss Map’s current 1D form. The performance of the stated work is evaluated using some of the most important metrics as well as the different attacks in the field. In addition to traditional and well-established metrics such as PSNR, MSE, SSIM, Information Entropy, NPCR, UACI, and Correlation Coefficient that have been used to validate encryption schemes, classification accuracy is also verified using transfer learning. The simulation was done on the MATLAB platform, and the classification accuracy after the encryption-decryption process is compared.

【 授权许可】

Unknown