نقشه جفت سازی-سینوس-لجستیک 2D برای رمزگذاری تصویر
Abstract
1- Introduction
2- 2D Logistic-Sine-coupling map
3- 2D-LSCM-based image encryption algorithm
4- Simulation results and efficiency analysis
5- Security analysis
6- Conclusion
References
Abstract
Image encryption is a straightforward strategy to protect digital images by transforming images into unrecognized ones. The chaos theory is a widely used technology for image encryption as it has many significant properties such as ergodicity and initial state sensitivity. When chaotic systems are used in image encryption, their chaos performance highly determines the security level. This paper presents a two-dimensional (2D) Logistic-Sine-coupling map (LSCM). Performance estimations demonstrate that it has better ergodicity, more complex behavior and larger chaotic range than several newly developed 2D chaotic maps. Utilizing the proposed 2D-LSCM, we further propose a 2D-LSCM-based image encryption algorithm (LSCM-IEA), which adopts the classical confusion-diffusion structure. A permutation algorithm is designed to permutate image pixels to different rows and columns while a diffusion algorithm is developed to spread few changes of plain-image to the whole encrypted result. We compare the efficiency of LSCM-IEA with several advanced algorithms and the results show that it has higher encryption efficiency. To show the superiority of LSCM-IEA, we also analyze the security of LSCM-IEA in terms of key security, ability of defending differential attack, local Shannon entropy and contrast analysis. The analysis results demonstrate that LSCM-IEA has better security performance than several existing algorithms.
Introduction
With the rapid development of digital technology, more and more multimedia information is generated and spread in the Internet [1]. Among all these multimedia information, digital image is an information format that can carry information with visualized way. For these digital images transmitted in networks, many of them are private images. For example, the personal medical images are usually private images, as they contain the information of personal healthy conditions. If these private images are obtained by some unauthorized ways, serious security disasters may happen. Thus, it is important to protect these private images [2–4] and image encryption is one efficient technology to protect them [5–8]. One strategy of encrypting image is to treat an image as a binary data stream and then use the developed data encryption algorithms to encrypt the data stream. These algorithms include the well-known data encryption standard [9], advanced encryption standard [10]. However, image data has many unique characteristics such as large data volume, high correlation and strong redundancy [11, 12]. Treating an image as a binary stream will miss these characteristics, and thus may make the encryption inefficient. To address this issue, many image encryption schemes considering image features have been proposed using various technologies, such as the chaos theory [13–16], DNA coding [17, 18], quantum theory [19, 20], compressive sensing [21, 22] and some mathematics models [23, 24]. Among these technologies, chaos theory is the most popular one. This is because chaotic behavior has many unique properties that are similar with the principles of image encryption [25–27]. Specifically, the ergodicity and initial state sensitivity of chaos theory correspond to the confusion and diffusion properties of encryption [28]. Some examples of chaos-based encryption schemes are as follows. In [29], Zhou et al. first proposed a new chaotic system that can use existing chaotic maps as seed maps to generate new chaotic maps, and then used one newly generated chaotic map to design an image encryption algorithm. In [30], Pak and Huang proposed a new color image encryption algorithm using the combination of Logistic, Sine and Chebyshev maps. In [21], Zhou et al. proposed a new image security scheme using hyperchaotic system and compressive sensing technology. This scheme can perform image encryption and image compression simultaneously.