Exploiting Dynamic Vector-Level Operations and a 2D-Enhanced Logistic Modular Map for Efficient Chaotic Image Encryption

Li, Hongmin; Yu, Shuqi; Feng, Wei; Chen, Yao; Zhang, Jing; Qin, Zhentao; Zhu, Zhengguo; Wozniak, Marcin

doi:10.3390/e25081147

Cited by 22 publications

(11 citation statements)

References 46 publications

(92 reference statements)

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“…In the proposed cryptosystem, the encrypted ciphertext comes from the output of the attention module. The key space [41,42,45] can be infinite because either the two sequences of single-pixel measurements or the input cryptographic key sequence are random with unlimited dynamic range.…”

Section: Optical Experiments Resultsmentioning

confidence: 99%

Optical Encryption Using Attention-Inserted Physics-Driven Single-Pixel Imaging

Yu,

Wang,

Shang

2024

Sensors

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Optical encryption based on single-pixel imaging (SPI) has made great advances with the introduction of deep learning. However, the use of deep neural networks usually requires a long training time, and the networks need to be retrained once the target scene changes. With this in mind, we propose an SPI encryption scheme based on an attention-inserted physics-driven neural network. Here, an attention module is used to encrypt the single-pixel measurement value sequences of two images, together with a sequence of cryptographic keys, into a one-dimensional ciphertext signal to complete image encryption. Then, the encrypted signal is fed into a physics-driven neural network for high-fidelity decoding (i.e., decryption). This scheme eliminates the need for pre-training the network and gives more freedom to spatial modulation. Both simulation and experimental results have demonstrated the feasibility and eavesdropping resistance of this scheme. Thus, it will lead SPI-based optical encryption closer to intelligent deep encryption.

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Section: Optical Experiments Resultsmentioning

confidence: 99%

Optical Encryption Using Attention-Inserted Physics-Driven Single-Pixel Imaging

Yu,

Wang,

Shang

2024

Sensors

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“…It focuses more on the technical details and experimental results of CIES-DVEM. To fully understand the limitations or drawbacks of the scheme, further analysis and evaluation would be required [51].…”

Section: Related Workmentioning

confidence: 99%

A Lightweight Multi-Chaos-Based Image Encryption Scheme for IoT Networks

Jain,

Titus,

Krishnan

et al. 2024

IEEE Access

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The swift development of the Internet of Things (IoT) has accelerated digitalization across several industries, offering networked applications in fields such as security, home automation, logistics, and quality control. The growth of connected devices, on the other hand, raises worries about data breaches and security hazards. Because of IoT devices' computational and energy limits, traditional cryptographic methods face issues. In this context, we emphasize the importance of our contribution to image encryption in IoT environments through the proposal of Multiple Map Chaos Based Image Encryption (MMCBIE), a novel method that leverages the power of multiple chaotic maps. MMCBIE uses multiple chaotic maps to construct a strong encryption framework that considers the inherent features of digital images. Our proposed method, MMCBIE, distinguishes itself by integrating multiple chaotic maps like Henon Chaotic Transform and 2D-Logistic Chaotic Transform in a novel combination, a unique approach that sets it apart from existing schemes. Compared to other chaotic-based encryption systems, this feature renders them practically indistinguishable from pure visual noise. Security evaluations and cryptanalysis confirm MMCBIE's highlevel security properties, indicating its superiority over existing image encryption techniques. MMCBIE demonstrated superior performance with NPCR (Number of Pixel Changing Rate) score of 99.603, UACI (Unified Average Changing Intensity) score of 32.8828, MSE (Mean Square Error) score of 6625.4198, RMSE (Root Mean Square Error) score of 80.0063, PSNR (Peak Signal to Noise Ratio) score of 10.2114, and other security analyses.

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“…MIEA-MPHM 7.9994 [48] 7.9992 [50] 7.9992 [51] 7.9993 [52] 7.9993 [53] 7.9993 [54] 7.9993 [55] 7.9976 [56] 7.9984…”

Section: Algorithm Iementioning

confidence: 99%

A Robust Memristor-Enhanced Polynomial Hyper-Chaotic Map and Its Multi-Channel Image Encryption Application

Qian,

Xiao,

Wei

et al. 2023

Micromachines

Self Cite

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Nowadays, the utilization of memristors to enhance the dynamical properties of chaotic systems has become a popular research topic. In this paper, we present the design of a novel 2D memristor-enhanced polynomial hyper-chaotic map (2D-MPHM) by utilizing the cross-coupling of two TiO2 memristors. The dynamical properties of the 2D-MPHM were investigated using Lyapunov exponents, bifurcation diagrams, and trajectory diagrams. Additionally, Kolmogorov entropy and sample entropy were also employed to evaluate the complexity of the 2D-MPHM. Numerical analysis has demonstrated the superiority of the 2D-MPHM. Subsequently, the proposed 2D-MPHM was applied to a multi-channel image encryption algorithm (MIEA-MPHM) whose excellent security was demonstrated by key space, key sensitivity, plaintext sensitivity, information entropy, pixel distribution, correlation analysis, and robustness analysis. Finally, the encryption efficiency of the MIEA-MPHM was evaluated via numerous encryption efficiency tests. These tests demonstrate that the MIEA-MPHM not only possesses excellent security but also offers significant efficiency advantages, boasting an average encryption rate of up to 87.2798 Mbps.

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Exploiting Dynamic Vector-Level Operations and a 2D-Enhanced Logistic Modular Map for Efficient Chaotic Image Encryption

Cited by 22 publications

References 46 publications

Optical Encryption Using Attention-Inserted Physics-Driven Single-Pixel Imaging

Optical Encryption Using Attention-Inserted Physics-Driven Single-Pixel Imaging

A Lightweight Multi-Chaos-Based Image Encryption Scheme for IoT Networks

A Robust Memristor-Enhanced Polynomial Hyper-Chaotic Map and Its Multi-Channel Image Encryption Application

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