RGB image encryption using hyper chaotic system

Kar, M.; Mandal, Mrinal Kanti; Nandi, Debashis

doi:10.1109/icrcicn.2017.8234534

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…The values of adjacent pixel correlation for our encryption algorithm in horizontal, vertical, and diagonal directions are 0.00260, −0.00290, and 0.00043. Analyzed from the horizontal direction, our encryption algorithm effect is better than that of the encryption algorithm of Reference [35][36][37][38], and the same as that of Reference [10]; analyzed from the vertical direction, our encryption algorithm effect is better than that of the encryption algorithm of Reference [10,[35][36][37][38]; analyzed from the diagonal direction, our encryption algorithm effect is better than that of the encryption algorithm of Reference [35][36][37][38], and close to that of the encryption algorithm of Reference [10]. In general, the encryption effect of our encryption algorithm is better than Reference [35][36][37][38] and close to Reference [10].…”

Section: Example 1 Image Encryption and Decryptionmentioning

confidence: 85%

Fixed-Time Synchronization of Neural Networks Based on Quantized Intermittent Control for Image Protection

et al. 2021

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This paper considers the fixed-time synchronization (FIXTS) of neural networks (NNs) by using quantized intermittent control (QIC). Based on QIC, a fixed-time controller is designed to ensure that the NNs achieve synchronization in finite time. With this controller, the settling time can be estimated regardless of initial conditions. After ensuring that the system has stabilized through this strategy, it is suitable for image protection given the behavior of the system. Meanwhile, the encryption effect of the image depends on the encryption algorithm, and the quality of the decrypted image depends on the synchronization error of NNs. The numerical results show that the designed controller is effective and validate the practical application of FIXTS of NNs in image protection.

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Section: Example 1 Image Encryption and Decryptionmentioning

confidence: 85%

Fixed-Time Synchronization of Neural Networks Based on Quantized Intermittent Control for Image Protection

et al. 2021

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“…The proposed system was validated using extensive experiments. Kumar et al in [59] introduced a technicality entirely based on four-dimensional Lorenz chaotic map. The authors generated a random number matrix to improve security by using the hyperchaotic system.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Hybrid Secure Image Encryption Based on Julia Set of Fractals and 3D Lorenz Chaotic Map

Masood

Ahmad

Shah

et al. 2020

Entropy

111

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Chaos-based encryption schemes have attracted many researchers around the world in the digital image security domain. Digital images can be secured using existing chaotic maps, multiple chaotic maps, and several other hybrid dynamic systems that enhance the non-linearity of digital images. The combined property of confusion and diffusion was introduced by Claude Shannon which can be employed for digital image security. In this paper, we proposed a novel system that is computationally less expensive and provided a higher level of security. The system is based on a shuffling process with fractals key along with three-dimensional Lorenz chaotic map. The shuffling process added the confusion property and the pixels of the standard image is shuffled. Three-dimensional Lorenz chaotic map is used for a diffusion process which distorted all pixels of the image. In the statistical security test, means square error (MSE) evaluated error value was greater than the average value of 10000 for all standard images. The value of peak signal to noise (PSNR) was 7.69(dB) for the test image. Moreover, the calculated correlation coefficient values for each direction of the encrypted images was less than zero with a number of pixel change rate (NPCR) higher than 99%. During the security test, the entropy values were more than 7.9 for each grey channel which is almost equal to the ideal value of 8 for an 8-bit system. Numerous security tests and low computational complexity tests validate the security, robustness, and real-time implementation of the presented scheme.Entropy 2020, 22, 274 2 of 28 scientist Claude Shannon in the year 1949, the same person introduced the property of confusion and diffusion. The property of randomness helped in securing digital multimedia information. The security of digital bitstream became one of the central issues when the data was transformed into a digital bitstream. The privacy of digital information posed a new problem in the digital world. The secure digital data in the form of binary bits are openly accessible for hackers. The data can be easily obtainable from the far site using the internet. It needs proper measurements to secure digital information over an insecure line of communication [1][2][3]. The digital data can be secured by hiding the identity of the original digital image over the secret cover image or another approach is pixels distortion or encryption. The first method of securing digital information is information hiding techniques and the study of steganography, which means the value of the data is preserved under the secret image. The image security is a very important issue as compared to textual security, where image pixels are to be examined concerning nearby pixels in a different orientation. The more pixels are dissimilar means the proposed encryption technique is more suitable for brute force attacks. Moreover, plain image pixels are always correlated to each other where an attacker can easily find secret information. Chaos-based encryption technique is preferred over some ...

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