A new Color image Encryption based on multi Chaotic Maps

Taqi, Ibtisam A.; Hameed, Sarab M.

doi:10.24996/ijs.2018.59.4b.17

Cited by 9 publications

(5 citation statements)

References 14 publications

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“…The UACI and NPCR measures as shown in Table 5 are used to determine the effect on the cipher image of the change of 1 bit/pixel in the original image [24], [25].…”

Section: The Information Entropymentioning

confidence: 99%

Artificial intelligence techniques for encrypt images based on the chaotic system implemented on field-programmable gate array

Al-Musawi

Al-Ibadi

Wali

2023

IJ-AI

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<span lang="EN-US">Image encryption is an important issue in protecting the content of images and in the area of information security. This article proposes a novel method for image encryption and decryption using the structure of the artificial neural network (ANN)-based chua chaotic system (CCS). This structure was efficiently designed on a field-programmable gate array (FPGA) chip utilizing the xilinx system generator (XSG) tool with the IEEE-754-1985 32-bit floating-point number format. For ANN-based CCS design, a multilayer feed forward neural network (FFNN) structure with three inputs and three outputs was created. This structure consists of one hidden layer with four neurons, each of which has a Tangent Sigmoid activation function. The training of ANN-based CCS yielded a 3.602e-13 mean square error (MSE) value. After successfully training the ANN-based CCS, the design was carried out on FPGA, utilizing the ANN structure's bias and weight values as a reference. The xilinx vivado (2017.4) design suite was used to synthesis and test the ANN-based CCS on the FPGA. The histogram, correlation coefficient, and entropy are used to perform security analysis on various images. Finally, FPGA hardware co-simulation using a Xilinx Artix7 xc7a100t-1csg324 chip was utilized to verify that the encryption and decryption of the images were successful.</span>

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“…The UACI and NPCR measures as shown in Table 5 are used to determine the effect on the cipher image of the change of 1 bit/pixel in the original image [24], [25].…”

Section: The Information Entropymentioning

confidence: 99%

Artificial intelligence techniques for encrypt images based on the chaotic system implemented on field-programmable gate array

Al-Musawi

Al-Ibadi

Wali

2023

IJ-AI

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“…The sensitivity of a cipher picture should be high before the original image or secret key is slightly changed. As in (11) and (12), UACI and NPCR measures as shown in Table 5 are used to determine the effect on the cipher image of the change of 1 bit/pixel in the original image [28], [29].…”

Section: Differential Attack Analysismentioning

confidence: 99%

Field-programmable gate array design of image encryption and decryption using Chua’s chaotic masking

Al-Musawi

Wali

Al-Ibadi

2022

IJECE

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<span>This article presents a simple and efficient masking technique based on Chua chaotic system synchronization. It includes feeding the masked signal back to the master system and using it to drive the slave system for synchronization purposes. The proposed system is implemented in a field programmable gate array (FPGA) device using the Xilinx system generator tool. To achieve synchronization, the Pecora-Carroll identical cascading synchronization approach was used. The transmitted signal should be mixed or masked with a chaotic carrier and can be processed by the receiver without any distortion or loss. For different images, the security analysis is performed using the histogram, correlation coefficient, and entropy. In addition, FPGA hardware co-simulation based Xilinx Artix7 xc7a100t-1csg324 was used to check the reality of the encryption and decryption of the images.</span>

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“…Several recent research works are leverage chaotic system capabilities to develop the encryption algorithm, especially, the PRNG module, for example, in [10] the encryption model mixes three chaotic systems, logistic, sine, and tent maps into one system CML, the proposed chaotic system was used as a key generator of 256 bits length, furthermore, the encryption model involved a bit-level z-scan scrambler module, this scheme might rein the performance. In [11] the key generator of the encryption model has been implemented using 1D logistic and sine maps and a combination of them. For use in distributed systems, however, this study didn't consider the improvement of the chaotic parameter's range.…”

Section: Related Workmentioning

confidence: 99%

New Pseudo-Random Key Generator for IoT-security Model Based on a Novel 3D Coupled Map Lattice

2022

IJIES

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In cryptography fields, one concern is the consideration of the capacity of the resource, especially, in environments that run IoT devices. This paper presents a lightweight and comprehensive security system, which involves a novel Pseudo-Random Number Generator (PRNG) based on a novel 3D Coupled Map Lattice system (3D-CML) as a chaotic system that is more practical in cryptography, and encryption algorithms depend on two security levels, firstly, permuting the plain image using a standard 2D Henon chaotic map, then, encrypt it by a One-Time Symmetric Key (OTSK). The bifurcation diagram of the 3D-CML shows that the chaotic parameters have been extended in their ranges, this feature has a positive effect on the key-space size which its size equal to 2^373. From another aspect, with fewer iteration configurations, all random sequences that are produced by the proposed PRNG have passed all statistical tests of the NIST suite, in turn, this configuration would lower the run-time, hence decreasing the computational effort as a response to the requirements of limited-resources environments, such as IoT. Several assessment metrics, such as Mutual Information, Gray Difference Degree, Histogram, Chi-square, Correlation Coefficient, Entropy, and more, confirmed that the proposed algorithms can be robust in dissolving the internal characteristics in the original image for producing an encryption image, resulting in strong resistance against any cyberattacks.

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A new Color image Encryption based on multi Chaotic Maps

Cited by 9 publications

References 14 publications

Artificial intelligence techniques for encrypt images based on the chaotic system implemented on field-programmable gate array

Artificial intelligence techniques for encrypt images based on the chaotic system implemented on field-programmable gate array

Field-programmable gate array design of image encryption and decryption using Chua’s chaotic masking

New Pseudo-Random Key Generator for IoT-security Model Based on a Novel 3D Coupled Map Lattice

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