Publications

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A Secure Spatio-temporal Chaotic Pseudorandom Generator for Image Encryption

Published in IEEE Transactions on Circuits and Systems for Video Technology , 2024

Digital image have become the main source of human information acquisition and exchange, which is widely used in aerospace, biomedical and military fields. Therefore, to ensure the secure transmission of digital image, this paper proposes a secure spatio-temporal chaotic pseudorandom generator for image encryption is proposed. Firstly, we consider the potential impact of precision loss in digital circuits on the degradation of chaotic systems. Therefore, we employ the unscented Kalman filter (UKF) to assess accuracy loss in both Logistic, Sine and Chebyshev maps, which is compensated for by introducing perturbations into the spatio-temporal chaotic system. Secondly, we design new Sine maps and Chebyshev maps with time-varying delays to perturb the time dimension of the non-adjacent coupled lattice and improve the complexity and security of the chaotic system. In the end, we use the newly designed spatio-temporal chaotic system as a pseudo-random generator to design a new image encryption scheme. In this paper, we present a security proof for the newly proposed spatio-temporal chaotic system and image encryption scheme. Furthermore, security experiments demonstrate that the spatiotemporal chaotic system and image encryption scheme presented in this paper exhibit improved uniform distribution, absence of chaos degradation or predictability issues while offering randomness suitable for engineering applications.

Recommended citation: Y. Wang, L. Chen, K. Yu and T. Fu, "A Secure Spatio-temporal Chaotic Pseudorandom Generator for Image Encryption," in IEEE Transactions on Circuits and Systems for Video Technology, doi: 10.1109/TCSVT.2024.3384297. keywords: {Encryption;Security;Generators;Lattices;Chaotic communication;Chaos;Degradation;UKF;Spatio-temporal chaotic;PRNG;Image encryption;Provably security},
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Multi-key spatio-temporal chaotic image encryption and retrieval scheme

Published in Nonlinear Dynamics, 2023

To protect privacy, many users opt to encrypt images prior to outsourcing them to cloud service platforms (CSPs). However, this encryption process results in the loss of image features and subsequent inability to retrieve them. We propose an image encryption and retrieval algorithm, which ensures that privacy is not leaked in both the upload and retrieval stages. First, overcoming the insufficient security and degradation in chaotic systems, we introduce the time-varying functions, and unscented Kalman filter to improve the non-adjacent coupled map lattice complexity and security. Secondly, considering the encryption efficiency, we compress the plaintext image to reduce the time of the encryption phase and improve the overall encryption speed. Finally, we use the locally sensitive hash (LSH) for feature vector dimensionality reduction to improve the retrieval efficiency and perform a secondary LSH on the reduced feature vector to form a new hash-key retrieval structure in the generate index phase, which improves the retrieval efficiency. The experimental results prove that our proposed encryption algorithm can meet the image encryption algorithms high retrieval accuracy and multi-user without revealing privacy.

Recommended citation: Wang, Y., Chen, L., Yu, K. et al. Multi-key spatio-temporal chaotic image encryption and retrieval scheme. Nonlinear Dyn 112, 3003–3025 (2024). https://doi.org/10.1007/s11071-023-09170-7
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Efficient and secure content-based image retrieval with deep neural networks in the mobile cloud computing

Published in Computers & Security, 2023

Smart devices offer a variety of more convenient forms to help us record our lives and generate a large amount of data in this process. Limited by the local storage capacity, many users outsource their image data directly to the cloud server. However, images stored in plaintext on the cloud server are very insecure, resulting in image privacy information can be easily leaked. Therefore, users will encrypt the images and outsource them to the cloud server, but the encrypted images cannot be retrieved. Therefore, we proposed a secure and efficient ciphertext image retrieval scheme based on image content retrieval (CBIR) and approximate homomorphic encryption (HE). First, we used approximate homomorphic encryption to encrypt images after resizing and uploaded the ciphertext images to the cloud for feature extraction of ciphertext. At the same time, the large images (size, dimension, and resolution) would generate data inflation after using homomorphic encryption. Therefore, the original images are encrypted using the chaotic image encryption scheme to reduce ciphertext size and computation costs. Second, we proposed two deepening network depth optimization strategies that address the problem of insufficient neural network depth. Finally, reducing the dimensionality of the ciphertext feature vector using locally sensitive hashing (LSH) can accelerate the retrieval of ciphertext images. Compared with other literature, our ciphertext image retrieval scheme can significantly reduce the rounds of user-server communication.

Recommended citation: Yu Wang, Liquan Chen, Ge Wu, Kunliang Yu, Tianyu Lu, Efficient and secure content-based image retrieval with deep neural networks in the mobile cloud computing, Computers & Security, Volume 128, 2023, 103163, ISSN 0167-4048, https://doi.org/10.1016/j.cose.2023.103163.
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An image encryption scheme based on logistic quantum chaos

Published in Entropy, 2022

This paper proposes an image encryption scheme based on logistic quantum chaos. Firstly, we use compressive sensing algorithms to compress plaintext images and quantum logistic and Hadamard matrix to generate the measurement matrix. Secondly, the improved flexible representation of the quantum images (FRQI) encoding method is utilized for encoding the compressed image. The pixel value scrambling operation of the encoded image is realized by rotating the qubit around the axis. Finally, the quantum pixel is encoded into the pixel value in the classical computer, and the bit-level diffusion and scrambling are performed on it. Numerical analysis and simulation results show that our proposed scheme has the large keyspace and strong key sensitivity. The proposed scheme can also resist standard attack methods such as differential attacks and statistical analysis.

Recommended citation: Wang, Y.; Chen, L.; Yu, K.; Gao, Y.; Ma, Y. An Image Encryption Scheme Based on Logistic Quantum Chaos. Entropy 2022, 24, 251. https://doi.org/10.3390/e24020251
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Image encryption algorithm based on lattice hash function and privacy protection

Published in Multimedia Tools and Applications , 2022

To solve the problem of lack of trust in cloud platforms, we propose an image encryption algorithm based on the Lattice Hash function and privacy protection in this paper. A new chaotic system using Tent Map and Sine Map is designed in our proposed scheme, which does not have a period window. Meanwhile, our algorithm uses a new chaos system to provide random matrices F for Lattice Hash functions. The image feature vector and the initial key are also employed to obtain the security key, which can be used as input to the Lattice Hash. Besides, our algorithm utilizes the Paillier cryptosystem to encrypt the feature vector as the ciphertext image index for higher security. In addition, the 2D-Line map is improved and applied as a function for scrambling pixel positions to avoid the problem of storing pixel subscripts. Simulation experiments and security analysis shows that our proposed algorithm can resist differential attacks, statistical attacks, brutal attacks. Therefore, our scheme has good security performance and allows precise search over the ciphertext.

Recommended citation: Wang, Y., Chen, L., Yu, K. et al. Image encryption algorithm based on lattice hash function and privacy protection. Multimed Tools Appl 81, 18251–18277 (2022). https://doi.org/10.1007/s11042-022-12714-5
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The design of keyed hash function based on CNN-MD structure

Published in Chaos, Solitons & Fractals, 2021

In this paper, we propose a new chaotic neural network called the Merkle–Damgaard (CNN-MD) iterative structure, which we designed from the perspective of resisting attacks, such as length extension, second original image, Joux multiple collision, and intermediate encounter attacks. A new chaotic hash function based on the CNN-MD is presented. First, CNN is used as the compression function of CNN-MD, which ensures both the unidirectionality of data compression and the sensitivity of the hash value. Furthermore, the multi-input single-output construction of the CNN can achieve highly efficient data compression. In addition, in the process of group encryption of data, we use the value padding of coupled lattice mapping to improve the alignment of the plaintext of the MD algorithm. This new alignment can enhance the hash function’s collision resistance and the CNN-MD sensitivity. Experimental and theoretical analyses show that our proposed hashing algorithm can resist attacks, such as second original image, brute force, and multiple collision attacks, better than MD5, SHA-1, and SHA-2 algorithms. In terms of speed, our proposed hashing algorithm can successfully handle existing commercial scenarios better than MD5, SHA-1, SHA-3, and other methods. Therefore, the hash algorithm proposed in this study can be applied to existing e-commerce scenarios.

Recommended citation: Yu Wang, Liquan Chen, Xingyuan Wang, Ge Wu, Kunliang Yu, Tianyu Lu, The design of keyed hash function based on CNN-MD structure, Chaos, Solitons & Fractals, Volume 152, 2021, 111443, ISSN 0960-0779, https://doi.org/10.1016/j.chaos.2021.111443.
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