In recent years, many researchers have leveraged various memristors to design many novel memristive chaotic systems with complex dynamics. Compared with other chaotic systems, applying these memristive chaotic systems to image encryption is expected to solve some key problems in this field. Therefore, exploiting a recently reported memristive chaotic system, this paper proposes a novel image encryption scheme based on the memristive chaotic system and combining bidirectional bit-level cyclic shift and dynamic DNA-level diffusion (IES-M-BD). First, a discrete memristive chaotic map is employed to generate chaotic sequences. Then, the plaintext image is shifted circularly on bit-level according to chaotic sequences and the hash value of the plaintext image. After that, the shifted matrix is recombined on the bit plane and encoded dynamically by DNA encoding rules. Next, dynamic DNA-level diffusion and DNA-level permutation are carried out in two rounds. Finally, the encrypted image is obtained after dynamic DNA decoding. Simulation tests and performance analyses are also carried out in this paper. The simulation results and the security analyses demonstrate that this encryption scheme has a high security level and can resist various attacks.
Image encryption is an effective way to protect image data. However, existing image encryption algorithms are still unable to strike a good balance between security and efficiency. To overcome the shortcomings of these algorithms, an image encryption algorithm based on plane-level image filtering and discrete logarithmic transformation (IEA-IF-DLT) is proposed. By utilizing the hash value more rationally, our proposed IEA-IF-DLT avoids the overhead caused by repeated generations of chaotic sequences and further improves the encryption efficiency through plane-level and three-dimensional (3D) encryption operations. Aiming at the problem that common modular addition and XOR operations are subject to differential attacks, IEA-IF-DLT additionally includes discrete logarithmic transformation to boost security. In IEA-IF-DLT, the plain image is first transformed into a 3D image, and then three rounds of plane-level permutation, plane-level pixel filtering, and 3D chaotic image superposition are performed. Next, after a discrete logarithmic transformation, a random pixel swapping is conducted to obtain the cipher image. To demonstrate the superiority of IEA-IF-DLT, we compared it with some state-of-the-art algorithms. The test and analysis results show that IEA-IF-DLT not only has better security performance, but also exhibits significant efficiency advantages.
The application of multimedia sensors is widespread, and people need to transmit images more securely and efficiently. In this paper, an image transmission scheme based on two chaotic maps is proposed. The proposed scheme consists of two parts, secure image transmission between sensor nodes and sink nodes (SIT-SS) and secure image transmission between sensor nodes and receivers (SIT-SR). For resource-constrained environments, SIT-SS utilizes Tent-Logistic Map (TLM) to generate chaotic sequences and adopts TLM-Driven permutation and transformation to confuse image pixels. Then the cipher image is obtained through TLM-Driven two-dimensional compressed sensing. Compared with existing schemes, the secret key design of SIT-SS is more reasonable and requires fewer hardware resources. When sampling ratio is greater than 0.6, its image reconstruction quality has obvious advantages. For environments with huge security threats, SIT-SR adopts dynamic permutation and confusion based on discrete logarithms to confuse the image and exploits dynamic diffusion based on discrete logarithms to generate final cipher image. Similarly, compared with some existing schemes, the design of SIT-SR is more practical, and the statistical characteristics of the cipher image are better. Finally, extensive simulation tests confirm the superiority of the proposed scheme.
This paper presents a new, dictionary-based method for hyperspectral image classification, which incorporates both spectral and contextual characteristics of a sample clustered to obtain a dictionary of each pixel. The resulting pixels display a common sparsity pattern in identical clustered groups. We calculated the image’s sparse coefficients using the dictionary approach, which generated the sparse representation features of the remote sensing images. The sparse coefficients are then used to classify the hyperspectral images via a linear SVM. Experiments show that our proposed method of dictionary-based, clustered sparse coefficients can create better representations of hyperspectral images, with a greater overall accuracy and a Kappa coefficient.
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