Region of interest encryption for color images based on a hyperchaotic system with three positive Lyapunov exponets

Xue, Hanwen; Du, Junping; Li, Shouliang; Wei, Ma

doi:10.1016/j.optlastec.2018.04.030

Cited by 43 publications

(23 citation statements)

References 23 publications

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“…They are called the chaotic system, which is characteristic of three or higher dimensions [34]. The systems are classified as chaotic if they possess at least one positive Lyapunov exponent, and hyper-chaotic if more than one positive Lyapunov exponent is exhibited [35]. Another notable feature of a chaotic system is the strange attractor.…”

Section: Classification Of Chaosmentioning

confidence: 99%

Review of Chaos Detection Techniques Performed on Chaotic Maps and Systems in Image Encryption

Muthu

Murali

2021

SN COMPUT. SCI.

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The security concern in recent times has been rising exponentially, this has lead for highly secured image transmission. Chaos-based image encryption is increasingly used to protect images with high degree of security than other encryption techniques. Hence, novel chaotic dynamical systems are developed for this purpose with more advanced features and models. Various chaos detection techniques are performed on them to examine and measure their complexity. This paper analyzes the frequency of the chaos tests performed on 25 discrete chaotic maps and 18 continuous chaotic systems and reviewed for their effectiveness. It is observed that the bifurcation diagram and Lyapunov exponent are the most highly used tests for chaotic maps. Whereas, phase portrait and bifurcation diagram are commonly used for chaotic systems. Tests such as 0-1 test, three-state test and some entropy tests are seldom used. Further, the dynamics of the chaotic systems are better studied than the chaotic maps. But in most cases of chaotic maps, the test is performed only for certain random control parameter range, and its dynamics beyond that range is unknown. Hence, it is crucial to realize three important facts. First, a complete examination of the novel system must be performed with the appropriate chaos detection technique. Second, the complete range of the chaotic and non-chaotic region of a system must be presented. Third, the strength of chaos must be examined. These results are essential for generating a highly secured nonlinear key for an encryption algorithm, which makes it resistant to various attacks.

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Section: Classification Of Chaosmentioning

confidence: 99%

Review of Chaos Detection Techniques Performed on Chaotic Maps and Systems in Image Encryption

Muthu

Murali

2021

SN COMPUT. SCI.

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“…Artificial neural networks (ANN) are applied to the detection of regions of interest (ROI), and a kind of solution for automatically selecting ROI regions is given in [16]. There are other excellent solutions to choose the privacy regions automatically, such as threshold segmentation, face detection, infrared targeting, and salient mapping [8], [17], [18]. However, most of the above schemes are affected by the fixed segmentation threshold and the fixed detection parameters, and cannot automatically adapt to the images with different structures and formats.…”

Section: Introductionmentioning

confidence: 99%

A Novel Lossless Medical Image Encryption Scheme Based on Game Theory With Optimized ROI Parameters and Hidden ROI Position

Zhou

2020

IEEE Access

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Medical images contain a large amount of patients' private information. The theft and destruction of medical images will cause irreparable losses to patients and medical institutions. In order to detect the region of interest(ROI) accurately, avoid leakage of ROI position information, and realize lossless recovery of transform domain encryption, we propose a novel lossless medical image encryption scheme based on game theory with optimized ROI parameters and hidden ROI position. In the encryption process, the ROI is a pixel-level transformed to achieve the lossless decryption of medical images and protect medical image information from loss. At the same time, the position information of the ROI is effectively hidden, and leakage of the position information during transmission is avoided. In addition, the quantum cell neural network(QCNN) hyperchaotic system generates random sequence to scramble and diffuse the ROI. Most important of all, the quantitative analysis method of ROI parameters is given, and the optimal balance between encryption speed and encryption security performance is achieved by using game theory. Simulation experiments and numerical analysis verify that the scheme achieves optimized and lossless encryption and decryption of images, and can flexibly and reliably protect the medical images of different types and structures against various attacks.

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“…In a dynamical system, the Lyapunov exponent (LE) is a quantity that characterizes the rate of separation of infinitesimally close trajectories and estimates the chaos of the system [13], and if a chaotic system has two or more positive LEs, the system is defined to be hyperchaotic [14]. Recent research has demonstrated that image encryption algorithms associated with hyperchaotic systems show greater security [15][16][17][18][19][20][21][22][23]. Among the approaches, 4-6D hyperchaotic systems are widely used to enhance image encryption.…”

Section: Introductionmentioning

confidence: 99%

“…Zhu and Sun proposed an image encryption algorithm with two-round encryption operations based on a 4D hyperchaotic system [16]. Xue et al applied a 5D hyperchaotic system with 3 positive LEs for region of interest encryption for color images [21]. Wu et al presented a new lossless encryption algorithm that used the 2D discrete wavelet transform (DWT) and a 6D hyperchaotic system in both frequency and spatial domains for color images, and the experimental results indicated that the proposed algorithm was effective and efficient [23].…”

Section: Introductionmentioning

confidence: 99%

Image Encryption Based on Dynamic Filtering and Bit Cuboid Operations

Xie

et al. 2019

Complexity

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As one of the most widely used media types, images play an important role in the era of the Internet. And hence how to enhance the security of images has become a hot topic in the field of information security. However, due to some intrinsic characteristics of images, image security is still a challenging task. For the purpose of coping with this issue, in this paper, we propose a novel algorithm that combines a hyperchaotic system, dynamic filtering, and bit cuboid operations, namely, DFBC, for image encryption. Specifically, the proposed DFBC consists of four steps: firstly, a 7D Lorenz hyperchaotic system is utilized to generate a pseudorandom sequence; secondly, variable 1D filters are derived from the pseudorandom sequence, and dynamic filtering is conducted on each pixel of an image; thirdly, a diffusion scheme is performed and then the image is transformed to a bit cuboid; and, finally, various types of permutation (rearranging, symmetry, rotation, zigzag, and global bit permutation) are performed on the bit cuboid. The experiments on several testing images demonstrate that the DFBC achieves state-of-the-art results in terms of several evaluation criteria, showing that the DFBC is promising for image encryption.

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Region of interest encryption for color images based on a hyperchaotic system with three positive Lyapunov exponets

Cited by 43 publications

References 23 publications

Review of Chaos Detection Techniques Performed on Chaotic Maps and Systems in Image Encryption

Review of Chaos Detection Techniques Performed on Chaotic Maps and Systems in Image Encryption

A Novel Lossless Medical Image Encryption Scheme Based on Game Theory With Optimized ROI Parameters and Hidden ROI Position

Image Encryption Based on Dynamic Filtering and Bit Cuboid Operations

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