An Accelerator-based Logistic Map Image Cryptosystems for Grayscale Images
The logistic map is a class of chaotic maps. It is still in use in image cryptography. The logistic map cryptosystem has two stages, namely permutation, and diffusion. These two stages being computationally intensive, the permutation relocates the pixels, whereas the diffusion rescales them. The research on refining the logistic map is progressing to make the encryption more secure. Now there is a need to improve its efficiency to enable such models to fit for high-speed applications. The new invention of accelerators offers efficiency. But the inherent data dependencies hinder the use of accelerators. This paper discusses the novelty of identifying independent data-parallel tasks in a logistic map, handing them over to the accelerators, and improving their efficiency. Among the two accelerator models proposed, the first one achieves peak efficiency using coalesced memory access. The other cryptosystem further improves performance at the cost of more execution resources. In this investigation, it is noteworthy that the parallelly accelerated logistic map achieved a significant speedup to the larger grayscale image used. The objective security estimates proved that the two stages of the proposed systems progressively ensure security.
Marion E Hines. Image scrambling technique, October 28 1975. US Patent 3,914,877.
Guanrong Chen, Yaobin Mao, and Charles K Chui. A symmetric image encryption scheme based on 3d chaotic cat maps. Chaos, Solitons & Fractals, 21(3):749–761, 2004.
Lingfeng Liu and Suoxia Miao. A new image encryption algorithm based on logistic chaotic map with varying parameter. SpringerPlus, 5(1):289, 2016.
Bin Wang, Yingjie Xie, Changjun Zhou, Shihua Zhou, and Xuedong Zheng. Evaluating the permutation and diffusion operations used in image encryption based on chaotic maps. Optik, 127(7):3541–3545, 2016.
Gang He, Wenqing Wu, Li Nie, Jun Wen, Cheng Yang, and Wenxin Yu. An improved image multi-dimensional chaos encryption algorithm based on cuda. In 2019 9th International Conference on Information Science and Technology (ICIST), pages 183–187. IEEE, 2019.
Qing Wu, Maksym Spiryagin, Colin Cole, and Tim McSweeney. Parallel computing in railway research. International Journal of Rail Transportation, 8(2):111–134, 2020.
Xingyuan Wang and Chuanming Liu. A novel and effective image encryption algorithm based on chaos and dna encoding. Multimedia Tools and Applications, 76(5):6229–6245, 2017.
Zhijuan Deng and Shaojun Zhong. A digital image encryption algorithm based on chaotic mapping. Journal of Algorithms & Computational Technology, 13:1748302619853470, 2019.
Ashwin Raman. Parallel processing of chaos-based image encryption algorithms. PhD thesis, UC Irvine, 2016.
Carlos Villaseñor, Eric F Gutierrez-Frias, Nancy Arana-Daniel, Alma Y Alanis, and Carlos Lopez-Franco. Parallel crossed chaotic encryption for hyperspectral images. Applied Sciences, 8(7):1183, 2018.
A Sheik Abdullah, TGR Abiramie Shree, P Priyadharshini, and T Saranya. Algorithm and design techniques–a survey. Global Journal of Computer Science and Technology, 2019.
Carlos Villaseñor, Javier Gomez-Avila, Nancy Arana-Daniel, Alma Y Alanis, and Carlos Lopez-Franco. Fast chaotic encryption for hyperspectral images. In Processing and Analysis of Hyperspectral Data. IntechOpen, 2019.
Mouna Afif, Yahia Said, and Mohamed Atri. Computer vision algorithms acceleration using graphic processors nvidia cuda. Cluster Computing, pages 1–13, 2020.
Sparsh Mittal and Shraiysh Vaishay. A survey of techniques for optimizing deep learning on gpus. Journal of Systems Architecture, 99:101635, 2019.
Yuan Yuan, Xiaomin Yang, Wei Wu, Hu Li, Yiguang Liu, and Kai Liu. A fast single-image super-resolution method implemented with cuda. Journal of Real-Time Image Processing, 16(1):81–97, 2019.
Bhabesh Deka, Sumit Datta, Helal Uddin Mullah, and Suman Hazarika. Diffusion-weighted and spectroscopic mri super-resolution using sparse representations. Biomedical Signal Processing and Control, 60:101941, 2020.
Leila Habibpour, Shamim Yousefi, M Zolfy Lighvan, and Hadi S Aghdasi. 1d chaos-based image encryption acceleration by using gpu. Indian journal of science and technology, 9(6):19–25, 2016.
Wai-Kong Lee, Raphael C-W Phan, Wun-She Yap, and Bok-Min Goi. Spring: a novel parallel chaos-based image encryption scheme. Nonlinear Dynamics, 92(2):575–593, 2018.
Wai Kong Lee. High Speed Computation Of Advanced Cryptographic Algorithms On Massively Parallel Architecture. PhD thesis, UTAR, 2018.
Aryan Saxena, Vatsal Agrawal, Rajdeepa Chakrabarty, Shubhjeet Singh, and J Saira Banu. Accelerating image encryption with aes using gpu: A quantitative analysis. In International Conference on Intelligent Systems Design and Applications, pages 372–380. Springer, 2018.
Lin You, Ersong Yang, and Guangyi Wang. A novel parallel image encryption algorithm based on hybrid chaotic maps with opencl implementation. Soft Computing, pages 1–15, 2020.
Neha Kishore and Priya Raina. Parallel cryptographic hashing: Developments in the last 25 years. Cryptologia, 43(6):504–535, 2019.
Myle Ott, Sergey Edunov, David Grangier, and Michael Auli. Scaling neural machine translation. arXiv preprint arXiv:1806.00187, 2018.
Eric Weisstein. Logistic Map – from Wolfram MathWorld, 2020(accessed June 24, 2020). https://mathworld.wolfram.com/LogisticMap.html.
Huanzhou Zhu, Ligang He, Matthew Leeke, and Rui Mao. Wolfgraph: The edge-centric graph processing on gpu. Future Generation Computer Systems, 111:552–569, 2020.
Álvaro Salinas, Claudio Torres, and Orlando Ayala. A fast and efficient integration of boundary conditions into a unified cuda kernel for a shallow water solver lattice boltzmann method. Computer Physics Communications, 249:107009, 2020.
Håvard H Holm, André R Brodtkorb, and Martin L Sætra. Gpu computing with python: Performance, energy efficiency and usability. Computation, 8(1):4, 2020.
Shahrukh Athar and Zhou Wang. A comprehensive performance evaluation of image quality assessment algorithms. Ieee Access, 7:140030–140070, 2019.
Joshua Caleb Dagadu, Jian-Ping Li, Fadia Shah, Nadir Mustafa, and Kamlesh Kumar. Dwt based encryption technique for medical images. In 2016 13th International computer conference on wavelet active media technology and information processing (ICCWAMTIP), pages 252–255. IEEE, 2016.
Nabil Ben Slimane, Kais Bouallegue, and Mohsen Machhout. Nested chaotic image encryption scheme using two-diffusion process and the secure hash algorithm sha-1. In 2016 4th International Conference on Control Engineering & Information Technology (CEIT), pages 1–5. IEEE, 2016.
Jansher Khan, Jawad Ahmad, and Seong Oun Hwang. An efficient image encryption scheme based on: Henon map, skew tent map and s-box. In 2015 6th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO), pages 1–6. IEEE, 2015.
M Essaid, I Akharraz, A Saaidi, and A Mouhib. A new image encryption scheme based on confusion-diffusion using an enhanced skew tent map. Procedia Computer Science, 127:539–548, 2018.
Zhongyun Hua, Binghang Zhou, and Yicong Zhou. Sine chaotification model for enhancing chaos and its hardware implementation. IEEE Transactions on Industrial Electronics, 66(2):1273–1284, 2018.
Xiaoling Huang and Guodong Ye. An image encryption algorithm based on time-delay and random insertion. Entropy, 20(12):974, 2018.
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600–612, 2004.
Copyright (c) 2021 Journal of Cyber Security and Mobility
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.