Image Encryption Algorithm Based on Tent Delay-Sine Cascade with Logistic Map

Zhang, Guidong; Ding, Weikang; Li, Lian

doi:10.3390/sym12030355

Cited by 39 publications

(20 citation statements)

References 30 publications

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“…In our work, we have also used the aforementioned properties of chaos and proposed a scheme for protection data from attackers. Two chaotic maps, nonlinear chaos map [42] and Logistic map [43], are used in the encryption process, which is discussed in more detail in the latter part of the paper.…”

Section: Related Workmentioning

confidence: 99%

“…Now shuffle rows and columns of each channel with the sequence obtain from the chaos map and store value in

, and

, respectively. Logistic map is written as [ 43 ]:

In the above equation,

and

are initial conditions of map. Iterate Logistic map

times and multiply the obtained value with

and save the result in a row matrix R .…”

Section: System Modelmentioning

confidence: 99%

“…Logistic map is written as [ 43 ]:

In the above equation,

and

are initial conditions of map. Iterate Logistic map

times and multiply the obtained value with

and save the result in a row matrix R .…”

Section: System Modelmentioning

confidence: 99%

See 2 more Smart Citations

Mobility Prediction-Based Optimisation and Encryption of Passenger Traffic-Flows Using Machine Learning

Asad

Ahmad

Hussain

et al. 2020

Sensors

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Information and Communication Technology (ICT) enabled optimisation of train’s passenger traffic flows is a key consideration of transportation under Smart City planning (SCP). Traditional mobility prediction based optimisation and encryption approaches are reactive in nature; however, Artificial Intelligence (AI) driven proactive solutions are required for near real-time optimisation. Leveraging the historical passenger data recorded via Radio Frequency Identification (RFID) sensors installed at the train stations, mobility prediction models can be developed to support and improve the railway operational performance vis-a-vis 5G and beyond. In this paper we have analysed the passenger traffic flows based on an Access, Egress and Interchange (AEI) framework to support train infrastructure against congestion, accidents, overloading carriages and maintenance. This paper predominantly focuses on developing passenger flow predictions using Machine Learning (ML) along with a novel encryption model that is capable of handling the heavy passenger traffic flow in real-time. We have compared and reported the performance of various ML driven flow prediction models using real-world passenger flow data obtained from London Underground and Overground (LUO). Extensive spatio-temporal simulations leveraging realistic mobility prediction models show that an AEI framework can achieve 91.17% prediction accuracy along with secure and light-weight encryption capabilities. Security parameters such as correlation coefficient (<0.01), entropy (>7.70), number of pixel change rate (>99%), unified average change intensity (>33), contrast (>10), homogeneity (<0.3) and energy (<0.01) prove the efficacy of the proposed encryption scheme.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Now shuffle rows and columns of each channel with the sequence obtain from the chaos map and store value in

, and

, respectively. Logistic map is written as [ 43 ]:

In the above equation,

and

are initial conditions of map. Iterate Logistic map

times and multiply the obtained value with

and save the result in a row matrix R .…”

Section: System Modelmentioning

confidence: 99%

See 1 more Smart Citation

Mobility Prediction-Based Optimisation and Encryption of Passenger Traffic-Flows Using Machine Learning

Asad

Ahmad

Hussain

et al. 2020

Sensors

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show abstract

“…Some of these methods are based on pixel-level permutation, and the nonequilibrium system with chaos was applied into the encryption by constructing some S-boxes [7]. Zhang et al [8] improved the chaotic map with delay and cascade. Meanwhile, bit-level permutation has been applied to some other algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Novel Asymmetric Hyperchaotic Image Encryption Scheme Based on Elliptic Curve Cryptography

et al. 2021

Self Cite

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Most of the image encryption schemes based on chaos have so far employed symmetric key cryptography, which leads to a situation where the key cannot be transmitted in public channels, thus limiting their extended application. Based on the elliptic curve cryptography (ECC), we proposed a public key image encryption method where the hash value derived from the plain image was encrypted by ECC. Furthermore, during image permutation, a novel algorithm based on different-sized block was proposed. The plain image was firstly divided into five planes according to the amount of information contained in different bits: the combination of the low 4 bits, and other four planes of high 4 bits respectively. Second, for different planes, the corresponding method of block partition was followed by the rule that the higher the bit plane, the smaller the size of the partitioned block as a basic unit for permutation. In the diffusion phase, the used hyperchaotic sequences in permutation were applied to improve the efficiency. Lots of experimental simulations and cryptanalyses were implemented in which the NPCR and UACI are 99.6124% and 33.4600% respectively, which all suggested that it can effectively resist statistical analysis attacks and chosen plaintext attacks.

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“…Before performing it, a pixel-level scrambling is also done to randomize the plain image pixels. Another example of simultaneous pixel confusion and diffusion was proposed in [13]. Both processes were implemented through horizontal confusion-diffusion followed by vertical confusion-diffusion with the help of a newly proposed chaotic map, i.e., tent delay-sine cascade with the logistic map.…”

Section: Introductionmentioning

confidence: 99%

A New Composite Fractal Function and Its Application in Image Encryption

Agarwal

2020

J. Imaging

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Fractal’s spatially nonuniform phenomena and chaotic nature highlight the function utilization in fractal cryptographic applications. This paper proposes a new composite fractal function (CFF) that combines two different Mandelbrot set (MS) functions with one control parameter. The CFF simulation results demonstrate that the given map has high initial value sensitivity, complex structure, wider chaotic region, and more complicated dynamical behavior. By considering the chaotic properties of a fractal, an image encryption algorithm using a fractal-based pixel permutation and substitution is proposed. The process starts by scrambling the plain image pixel positions using the Henon map so that an intruder fails to obtain the original image even after deducing the standard confusion-diffusion process. The permutation phase uses a Z-scanned random fractal matrix to shuffle the scrambled image pixel. Further, two different fractal sequences of complex numbers are generated using the same function i.e. CFF. The complex sequences are thus modified to a double datatype matrix and used to diffuse the scrambled pixels in a row-wise and column-wise manner, separately. Security and performance analysis results confirm the reliability, high-security level, and robustness of the proposed algorithm against various attacks, including brute-force attack, known/chosen-plaintext attack, differential attack, and occlusion attack.

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Image Encryption Algorithm Based on Tent Delay-Sine Cascade with Logistic Map

Cited by 39 publications

References 30 publications

Mobility Prediction-Based Optimisation and Encryption of Passenger Traffic-Flows Using Machine Learning

Mobility Prediction-Based Optimisation and Encryption of Passenger Traffic-Flows Using Machine Learning

A Novel Asymmetric Hyperchaotic Image Encryption Scheme Based on Elliptic Curve Cryptography

A New Composite Fractal Function and Its Application in Image Encryption

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