Color image encryption based on joint permutation and diffusion

Li, Taiyong; Shi, Junqin; Zhang, Duzhong

doi:10.1117/1.jei.30.1.013008

Cited by 36 publications

(21 citation statements)

References 35 publications

(42 reference statements)

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“…To encrypt a color image, article [9] suggested a technique that Jointly Permutes and Diffuses (JPD) the pixels. Encryption methods are always constructed virtually by generating sequences from 4D hyperchaotic systems with positive Lyapunov exponents.…”

Section: Related Workmentioning

confidence: 99%

“…In terms of image encryption, most existing techniques also use permutation and diffusion, and they either apply permutation first, then diffusion, or vice versa. That is to say: they use permutation and diffusion to encrypt images independently [3] [9]. Traditional text encryption algorithms like RSA, DES, and AES show their immaturity of image encryption due to the large size and variety of image data storage structures [10] [11].…”

Section: Introductionmentioning

confidence: 99%

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Provably Secure and Fast Color Image Encryption Algorithm Based on S-Boxes and Hyperchaotic Map

Abduljabbar

Abduljaleel

et al. 2022

IEEE Access

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The World Wide Web is experiencing a daily increase in data transmission because of developments in multimedia technologies. Consequently, each user should prioritize preventing illegal access of this data by encrypting it before moving it over the Internet. Numerous color image encryption schemes have been developed to protect data security and privacy, indifferent to the computation cost. However, most of these schemes have high computational complexities. This research proposes a fast color image scrambling and encryption algorithm depending on different chaotic map types and an S-box that relies on a hyperchaotic map principle. The first step involves converting color image values from decimal representation to binary representation in the scrambling stage by changing the location of the bits according to a proposed swapping algorithm. Next, in the second scrambling stage, the same process occurs after returning color image values from binary representation to decimal representation and generating an Sbox with the assistance of two types of chaotic map, namely, a 2D Zaslavsky map and a 3D Hénon map. Thus, this S-box is relied upon to swap the locations of the pixels in the color image. The encryption procedure begins with the production of three key matrices using a hybrid technique that employs two low-complexity types of chaotic map, namely, a 1D Logistic map and a 3D Hénon map, followed by an XORed as a lightweight process between each key generated for the three matrices and the corresponding red, green, and blue image channels. According to the findings, the proposed scheme demonstrates the most efficiency in terms of lowering the computational cost and shows its effectiveness against a wide range of cryptographic attacks.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Provably Secure and Fast Color Image Encryption Algorithm Based on S-Boxes and Hyperchaotic Map

Abduljabbar

Abduljaleel

et al. 2022

IEEE Access

View full text Add to dashboard Cite

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“…A chaotic ant colony optimization (CACO) algorithm is a combination of chaos search and the ant colony optimization algorithm. After the ant colony search is completed, chaos is used to improve the search accuracy and avoid falling into the local optimum [28][29][30][31][32][33][34][35][36][37][38][39][40]. Therefore, the CACO algorithm takes on a global optimization ability.…”

Section: Chaotic Ant Colony Optimization Algorithmmentioning

confidence: 99%

Fault Diagnosis Using Cascaded Adaptive Second-Order Tristable Stochastic Resonance and Empirical Mode Decomposition

Cui

Guan

2021

Applied Sciences

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Aiming at the problems of poor decomposition quality and the extraction effect of a weak signal with strong noise by empirical mode decomposition (EMD), a novel fault diagnosis method based on cascaded adaptive second-order tristable stochastic resonance (CASTSR) and EMD is proposed in this paper. In the proposed method, low-frequency interference components are filtered by using high-pass filtering, and the restriction conditions of stochastic resonance theory are solved by using an ordinary variable-scale method. Then, a chaotic ant colony optimization algorithm with a global optimization ability is employed to adaptively adjust the parameters of the second-order tristable stochastic resonance system to obtain the optimal stochastic resonance, and noise reduction pretreatment technology based on CASTSR is developed to enhance the weak signal characteristics of low frequency. Next, the EMD is employed to decompose the denoising signal and extract the characteristic frequency from the intrinsic mode function (IMF), so as to realize the fault diagnosis of rolling bearings. Finally, the numerical simulation signal and actual bearing fault data are selected to prove the validity of the proposed method. The experiment results indicate that the proposed fault diagnosis method can enhance the decomposition quality of the EMD, effectively extract features of weak signals, and improve the accuracy of fault diagnosis. Therefore, the proposed fault diagnosis method is an effective fault diagnosis method for rotating machinery.

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“…The above methods use SR to play an important role in practical engineering applications. Other signal analysis methods are also proposed to process signals [21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

A Novel Advancing Signal Processing Method Based on Coupled Multi-Stable Stochastic Resonance for Fault Detection

et al. 2021

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In recent years, methods for detecting motor bearing faults have attracted increasing attention. However, it is very difficult to detect the faults from weak motor bearing signals under the strong noise. Stochastic resonance (SR) is a popular signal processing method, which can process weak signals with the noise, but the traditional SR is burdensome in determining its parameters. Therefore, in this paper, a new advancing coupled multi-stable stochastic resonance method, with two first-order multi-stable stochastic resonance systems, namely CMSR, is proposed to detect motor bearing faults. Firstly, the effects of the output signal-to-noise ratio (SNR) for system parameters and coupling coefficients are analyzed in-depth by numerical simulation technology. Then, the SNR is considered as the fitness function for the seeker optimization algorithm (SOA), which can adaptively optimize and determine the system parameters of the SR by using the subsampling technique. An advancing coupled multi-stable stochastic resonance method is realized, and the pre-processed signal is input into the CMSR to detect the faults of motor bearings by using Fourier transform. The faults of motor bearings are determined according to the output signal. Finally, the actual vibration data of induction motor bearings are used to prove the effectiveness of the proposed CMSR. The comparison results with the MSR show that the CMSR can obtain a higher output SNR, which is more beneficial to extract weak signal features and realize fault detection. At the same time, this method also has practical application value for engineering rotating machinery.

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Color image encryption based on joint permutation and diffusion

Cited by 36 publications

References 35 publications

Provably Secure and Fast Color Image Encryption Algorithm Based on S-Boxes and Hyperchaotic Map

Provably Secure and Fast Color Image Encryption Algorithm Based on S-Boxes and Hyperchaotic Map

Fault Diagnosis Using Cascaded Adaptive Second-Order Tristable Stochastic Resonance and Empirical Mode Decomposition

A Novel Advancing Signal Processing Method Based on Coupled Multi-Stable Stochastic Resonance for Fault Detection

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