Quantum-inspired metaheuristic algorithms: comprehensive survey and classification

To protect image information security, we propose an encryption algorithm based on chaotic sequences and bits' cross-diffusion. First, two chaotic sequences are generated by two-dimensional logic-sine coupling mapping, and the original image is scrambled with the two sequences. Secondly, the scrambled image is transformed into a one-dimensional sequence, and the low-order bits between every two pixels are fused to change the detailed information of the image, improved the ability to resist differential attacks. Finally, chaotic sequences are generated by iterative logical mapping to perform pixel replacement and ciphertext diffusion. The known plaintext attack can be effectively resisted because of the stochastic combination of diffusion scheme and chaotic sequence. It also has a significant effect on resisting differential attack. Take Lena image as an example, NPCR and UACI can reach 99.6063% and 33.4477%, respectively, which are very close to the ideal value. The encryption scheme has good key sensitivity and strong ability to resist statistical attacks, which shows that the algorithm has adequate security.

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A Novel Image Encryption Algorithm Based on Chaotic Sequences and Cross-Diffusion of Bits

Segmentation of sonar images with intensity inhomogeneity based on improved MRF

Side-scan sonar is an important application in the field of ocean exploration. Accurate segmentation of target regions in side-scan sonar images is a challenging issue due to the low-resolution and strong noise interference. To accurately and faster segment the different categories target in sonar image, a novel convolutional neural networks (CNNs) model is proposed in this study. Firstly, the deep separable residual module is used for target regions multi-scale feature extraction and suppression noise feature information interference, and the multi-channel feature fusion method is used to enhance feature information transfer of convolution layers. Secondly, the adaptive supervised function is used for pixel-wise classification of different categories targets. Finally, to improve model generalization ability and robustness, the adaptive transfer learning method is introduced in the model training process. We have performed extensive experiments on side-scan sonar image with different targets and scales. The experimental results show that the detection accuracy of the proposed method reaches 95.73%, which is outperforms other state-of-the-art methods on the side-scan sonar image segmentation tasks. Moreover, the method has fewer computational parameters, facilitating future deployment it to underwater mobile detection devices.

Side-Scan Sonar Image Segmentation Based on Multi-Channel Fusion Convolution Neural Networks

With the proliferation of Internet-of-Things (IoT) devices at public facilities, work places, homes, and beyond, securing data communications is becoming increasingly challenging. This paper describes the analysis and practical implementation of synchronized hyperchaotic maps for securing short-range data links in IoT devices. The data is stream-encrypted in the physical layer using chaotic masking and decrypted using a synchronized chaotic map. Two different energy-efficient chaotic encryption schemes are proposed: 1) direct sampling and masking of analog sensor outputs, thus avoiding the need for analog-to-digital (ADC) conversion; and 2) bit scrambling and masking of digital data. Both schemes were initially investigated and simulated in MATLAB. The effective number of encryption keys when using such hyperchaotic maps was also studied. Simulation results were validated by implementing two maps on circuit boards using high-speed discrete components. Experimental results for digital communication show a bit error rate (BER) of $\approx 2\times 10^{-6}$ at a bit rate of 10 kbps and a clock frequency of 0.5 MHz, making the approach feasible for high-fidelity real-time speech and image transmission without additional error control coding. An energy-efficient on-chip chaotic encryption system was developed using analog current-mode circuits and has a simulated power consumption of $ in the TSMC 180 nm CMOS process, which is sufficiently low for use in many battery-less miniaturized IoT sensor nodes.

Encrypted Physical Layer Communications Using Synchronized Hyperchaotic Maps

Phase retrieval from single interference fringe is important and effective method in obtaining the real phase distribution. The original phase can be retrieved by the line integral of its gradient expressed as sine and cosine components, which were gained by the Hilbert transform twice from a single interference fringe pattern. However, this method fails when the phase transformation of the interference fringe is too fast. In this paper, a novel method to recover the continuous phase of the whole field is proposed to solve the above problems. The shear interference technique is introduced into the phase retrieval method to build an exponential 2-D complex light field of natural base for the phase slope obtained by the Hilbert transform. Then, the expressions of phase slopes in x- and y-directions are constructed as a discrete Poisson equation. Therefore, the calculation of phase retrieval is equivalent to solve the discrete Poisson equation mathematically. Finally, the real phase is gotten by the weighted discrete cosine transform (WDCT) of the discrete Poisson equation. The simulation results verify the validity of this method and show that the proposed method can achieve the phase retrieval of the phase discontinuity in x- and y-directions, which leads to the under-sampled problem. It can restore the whole field phase distribution rapidly and accurately. Moreover, this method is applied to phase retrieval of interferometric synthetic aperture radar (InSAR) with the under-sampled problem in this paper. The experimental results show that this method can recover the phase of InSAR with the under-sampled problems caused by terrain abrupt change and so on. Compared with other commonly used methods, it achieved satisfactory results. This method provides a new idea for solving the under-sampled problem in the phase retrieval from a single-frame interference fringe.

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Under-Sampled Phase Retrieval of Single Interference Fringe Based on Hilbert Transform

Aiming at the problem that the existing bit scrambling encryption algorithm is not sensitive to the bit scrambling between 8 bits of one pixel, and the anti-noise and anti-selective plaintext attack ability is weak, a 3D cyclic shift bit scrambling image encryption algorithm is proposed in this paper. Discarding the scrambling mode of 8 bits of one pixel and the bits of all pixels, the pixel values are converted into binary arrays and then converted into 3D matrices in this paper. And the higher bit-planes and the lower bit-planes, which contain the plaintext information of different weights, are scrambled by cyclic shifting respectively. Therefore the sensitivity of the bit scrambling, the anti-noise attack ability of the algorithm and the randomness of intermediate ciphertext are improved. Moreover the randomness and the anti-noise ability can be adjusted by changing the number of higher bit-planes according to different encryption requirements. A new type of Logistic-Fibonacci (L-F) cascade chaos is constructed to generate random sequences, which solves the problem of blank windows in the uneven distribution of Logistic chaos. The initial value and the control parameters are increased, the sequence randomness is improved, and the fastness of low-dimensional chaos is preserved. By strongly correlating the key with SHA-256 of the plaintext, the key stream can change adaptively with the plaintext, which greatly improves the sensitivity of the plaintext and the ability of resisting the selective plaintext and ciphertext attack. The experiments show that the algorithm can encrypt all kinds of images with high efficiency, and can resist common attacks. It is a secure and reliable image encryption algorithm.

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An Image Encryption Algorithm Based on Logistic-Fibonacci Cascade Chaos and 3D Bit Scrambling

The underwater sonar image has the characteristics of complex background and heavy noise pollution. By using multi-bit quantum system to encode particles, a new sonar image segmentation algorithm based on quantum-inspired particle swarm and fuzzy clustering is proposed. Based on its own optimal particles and global optimal particles, the rotation angle is determined. By calculating the variance of the particle group fitness, the multi-bit quantum revolving gate is used to update the particle position in real time. The output of improved particle swarm optimization is used to initialize the K-mean clustering center to converge to the global optimal solution. Based on the idea of fuzzy membership matrix in FCM, combined with the isolated spatial information characteristics of the noise, the sonar image segmentation and the denoising are carried out. The experimental results show that the proposed algorithm can improve the global search ability of particle swarm optimization effectively. It is better than the quantum fuzzy clustering and quantum genetic algorithm in image segmentation. The analysis results of multiple real underwater sonar images show that the new optimization algorithm has faster convergence speed, better optimizing capacity and better segmentation results for sonar images.

A sonar image segmentation algorithm based on quantum-inspired particle swarm optimization and fuzzy clustering

The invention discloses a hybrid chaotic virtual optical image encryption method, which comprises the following steps of: 1, assuming that the size of a plaintext image is M * N, and sequentially reading pixel values of each point of the image from left to right to obtain a pixel matrix F (x, y); step 2, performing phase encoding on the plaintext image F (x, y) to obtain Q (x, y); step 2, modulating Q (x, y) through a random phase template RM1 to obtain F1 (x, y); step 3, carrying out Fourier transform on F1 (x, y), and carrying out random phase template RM2 modulation to obtain F2 (alpha, y);step 4, carrying out inverse Fourier transform on F2 (alpha beta) to obtain a ciphertext image U1; and step 5, performing secondary encryption of pixel value substitution on U1 (x, y) to obtain a ciphertext image U2. According to the invention, a coparametric generalized Fibonacci chaotic system and a coupled tent space-time chaotic system are adopted to respectively construct random templates with good uniformity; therefore, the two chaotic systems have a linkage effect, the defect that the distribution of random template elements constructed by the Logistic chaotic system is not uniform isovercome, and the hiding property on a ciphertext and the sensitivity on a secret key are enhanced.

Hybrid chaotic virtual optical image encryption method

: Aiming at the problems of long time-consuming in image compression encryption and color distortion of the decrypted image, a fast and safe general image encryption algorithm is proposed. The compression network uses bilinear interpolation and a fully connected layer for compression preprocessing to prevent the loss of color information. Convolution layer improves compression rate and reduces time complexity in this algorithm. In the encryption part, the chaotic sequence dynamically selects the pixel block and the pixel points in the block combining the XOR operation to perform pixel diffusion, which can improve the randomness of the algorithm. In the improved bilinear convolution neural network, bilinear and convolutional layers are used to obtain the image contour features. And the fully connected layer obtains color information for image reconstruction. This algorithm realizes the integration of compression, encryption, decryption and restoration of grayscale and RGB format color images. The experimental results show that the various encryption indexes of this algorithm are close to ideal values. It has good encryption security and anti-attack ability. Moreover, the encryption time is greatly shortened. On the 500 test set (BSD500) commonly used in image processing, when the sampling rate is 0.25, 0.10, 0.04 and 0.01, the peak signal-to-noise ratio can reach to 29.01 dB, 25.69 dB,

Yuan Guo

Papers

Under-Sampled Phase Retrieval of Single Interference Fringe Based on Hilbert Transform

An Image Encryption Algorithm Based on Logistic-Fibonacci Cascade Chaos and 3D Bit Scrambling

A sonar image segmentation algorithm based on quantum-inspired particle swarm optimization and fuzzy clustering

Hybrid chaotic virtual optical image encryption method

General Image Fast Encryption Algorithm for Deep Learning Compression and Reconstruction