Comparative evaluations of image encryption algorithms

• Main Author: Liu, Zhe (Author)
• Other Authors: Yan, Wei Qi (Contributor), Yang, Bobby (Contributor)
• Format: Others
• Published: Auckland University of Technology, 2018-06-18T02:52:37Z.
• Subjects: Image encryption; Double random phase encoding; Chaotic scrambling; Logistic map; Image denoising; Linear CNN model; Thesis
• Online Access: Get fulltext

 Information security has become a significant issue for protecting the secret information during transmission in practical applications in the era of information. A raft of information security schemes have been used in image encryption. These schemes can be divided into two domains; the first one encrypts the images based on spatial domain, the typical method of spatial image encryption technology is in use of chaotic system, most of the earlier encryption methods are belong to this domain; the other encrypts images on frequency domain, most of the optical image encryption methods are processed in this domain. In this thesis, a slew of approaches for image encryption have been proposed. The contributions of this thesis are listed as follows. (1) We design the improved encryption method based on traditional Double Random Phase Encoding (DRPE) method and use Discrete Cosine Transform (DCT) to replace Discrete Fourier Transform (DFT) so as to avoid operations on complex numbers; we use a logistic map to generate random matrices instead of random phase masks in the traditional DRPE so as to decrease the size of secret keys. (2) We design the encryption method based on traditional watermarking scheme by using Discrete Wavelet Transform (DWT), DCT and Singular Value Decomposition (SVD) together, the proposed DWT-DCT-SVD method has higher robustness than traditional chaotic scrambling method and DRPE method. (3) We improve the DWT-DCT-SVD method by using denoising techniques and design the denoising method based on Convolutional Neural Networks (CNN); the improved method has higher robustness against noise attacks.