Efficient neural chaotic generator for image encryption

Kassem, Ali; Hassan, Hussein Al Haj; Harkouss, Youssef; Assaf, Rima

doi:10.1016/j.dsp.2013.11.004

Cited by 32 publications

(19 citation statements)

References 15 publications

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“…As it can be seen, the obtained correlation values are better than [2,19] and [23] at least in two of the three dimensions. Also compared to other works described earlier such as [13,16] and [9], we get better results in all the dimensions.…”

Section: Correlation Of Two Adjacent Pixelsmentioning

confidence: 50%

“…The average values for the three chaotic outputs of our generator are shown in Table 5. Table 5 shows that the generated sequences have good 0/1 balance properties comparing to the results obtained in [13] and [19]. Note that the best results obtained, for the 100 sequences tested, are 50, 50.001 and 50 for the three sequences PPP, PBS and PBP.…”

Section: /1 Balance Testmentioning

confidence: 63%

“…Also, the encryption method used is just to XOR the chaotic bits with the image pixel bits which is not very secure. Kassem et al [13] use artificial neural networks as a source of perturbation for their proposed generator. The neural network scheme is first trained with PWLCM datasets.…”

Section: Neural Network Based Encryptionmentioning

confidence: 99%

“…More recently, researchers have showed interest in combining neural network and chaos to develop enhanced encryption algorithms [4,5,13,18,19]. The authors in [13] have trained the neural network with chaos to replace the chaotic generator in the encryption algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…The authors in [13] have trained the neural network with chaos to replace the chaotic generator in the encryption algorithm. In [19], the chaotic map is used as transfer function for each neuron.…”

Section: Introductionmentioning

confidence: 99%

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A new image encryption algorithm based on heterogeneous chaotic neural network generator and dna encoding

Maddodi

Awad

et al. 2018

Multimed Tools Appl

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This paper presents a new combined neural network and chaos based pseudorandom sequence generator and a DNA-rules based chaotic encryption algorithm for secure transmission and storage of images. The proposed scheme uses a new heterogeneous chaotic neural network generator controlling the operations of the encryption algorithm: pixel position permutation, DNA-based bit substitution and a new proposed DNA-based bit permutation method. The randomness of the generated chaotic sequence is improved by dynamically updating the control parameters as well as the number of iterations of the chaotic functions in the neural network. Several tests including auto correlation, 0/1 balance and NIST tests are performed to show high degree of randomness of the proposed chaotic generator. Experimental results such as pixel correlation coefficients, entropy, NPCR and UACI etc. as well as security analyses are given to demonstrate the security and efficiency of the proposed chaos based genetic encryption method.

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