Compressive-sensing-based double-image encryption algorithm combining double random phase encoding with Josephus traversing operation

A double-image encryption scheme based on compressive sensing is designed by combining a double random phase encoding technique with Josephus traversing operation. Two original images are first compressed and encrypted by compressive sensing in the discrete wavelet domain and then connected into a complex image according to the order of the alternate rows. Moreover, the resulting image is re-encrypted into stationary white noise by a double random phase encoding technique. Lastly, Josephus traversing method is utilized to scramble the transformed image. The initial states of the Henon chaotic map are the secret keys of this double-image encryption algorithm, which can be used to control the construction of the measurement matrix in compressive sensing and generation of the random-phase mask in double random phase encoding. Simulation results show that the proposed double-image encryption algorithm is effective and secure

Steganography with High Reconstruction Robustness: Hiding of Encrypted Secret Images

As one of the important methods to protect information security, steganography can ensure the security of data in the process of information transmission, which has attracted much attention in the information security community. However, many current steganography algorithms are not sufficiently resistant to recent steganalysis algorithms, such as deep learning-based steganalysis algorithms. In this manuscript, a new steganography algorithm, based on residual networks and pixel shuffle, is proposed, which combines image encryption and image hiding, named Resen-Hi-Net, an algorithm that first encrypts a secret image and then hides it in a carrier image to produce a meaningful container image. The proposed Resen-Hi-Net has the advantages of both image encryption and image hiding. The experimental results showed that the proposed Resen-Hi-Net could realize both image encryption and image hiding; the visual container image quality was as high as 40.19 dB on average in PSNR to reduce the possibility of being attacked, and the reconstructed secret image quality was also good enough (34.39 dB on average in PSNR). In addition, the proposed Resen-Hi-Net has a strong ability to resist destructive attacks and various steganographic analyses

Multi-bit quantum random number generation by measuring positions of arrival photons

We report upon the realization of a novel multi-bit optical quantum random number generator by continuously measuring the arrival positions of photon emitted from a LED using MCP-based WSA photon counting imaging detector. A spatial encoding method is proposed to extract multi-bits random number from the position coordinates of each detected photon. The randomness of bits sequence relies on the intrinsic randomness of the quantum physical processes of photonic emission and subsequent photoelectric conversion. A prototype has been built and the random bit generation rate could reach 8 Mbit/s, with random bit generation efficiency of 16 bits per detected photon. FPGA implementation of Huffman coding is proposed to reduce the bias of raw extracted random bits. The random numbers passed all tests for physical random number generator. (C) 2014 AIP Publishing LLC

Multi-Step-Ahead Wind Speed Forecast Method Based on Outlier Correction, Optimized Decomposition, and DLinear Model

Precise and dependable wind speed forecasting (WSF) enables operators of wind turbines to make informed decisions and maximize the use of available wind energy. This study proposes a hybrid WSF model based on outlier correction, heuristic algorithms, signal decomposition methods, and DLinear. Specifically, the hybrid model (HI-IVMD-DLinear) comprises the Hampel identifier (HI), the improved variational mode decomposition (IVMD) optimized by grey wolf optimization (GWO), and DLinear. Firstly, outliers in the wind speed sequence are detected and replaced with the HI to mitigate their impact on prediction accuracy. Next, the HI-processed sequence is decomposed into multiple sub-sequences with the IVMD to mitigate the non-stationarity and fluctuations. Finally, each sub-sequence is predicted by the novel DLinear algorithm individually. The predictions are reconstructed to obtain the final wind speed forecast. The HI-IVMD-DLinear is utilized to predict the real historical wind speed sequences from three regions so as to assess its performance. The experimental results reveal the following findings: (a) HI could enhance prediction accuracy and mitigate the adverse effects of outliers; (b) IVMD demonstrates superior decomposition performance; (c) DLinear has great prediction performance and is suited to WSF; and (d) overall, the HI-IVMD-DLinear exhibits superior precision and stability in one-to-four-step-ahead forecasting, highlighting its vast potential for application

Steganography with High Reconstruction Robustness: Hiding of Encrypted Secret Images

As one of the important methods to protect information security, steganography can ensure the security of data in the process of information transmission, which has attracted much attention in the information security community. However, many current steganography algorithms are not sufficiently resistant to recent steganalysis algorithms, such as deep learning-based steganalysis algorithms. In this manuscript, a new steganography algorithm, based on residual networks and pixel shuffle, is proposed, which combines image encryption and image hiding, named Resen-Hi-Net, an algorithm that first encrypts a secret image and then hides it in a carrier image to produce a meaningful container image. The proposed Resen-Hi-Net has the advantages of both image encryption and image hiding. The experimental results showed that the proposed Resen-Hi-Net could realize both image encryption and image hiding; the visual container image quality was as high as 40.19 dB on average in PSNR to reduce the possibility of being attacked, and the reconstructed secret image quality was also good enough (34.39 dB on average in PSNR). In addition, the proposed Resen-Hi-Net has a strong ability to resist destructive attacks and various steganographic analyses

Neurons in Primary Motor Cortex Encode External Perturbations during an Orientation Reaching Task

When confronting an abrupt external perturbation force during movement, subjects continuously adjust their behaviors to adapt to changes. Such adaptation is of great importance for realizing flexible motor control in varied environments, but the potential cortical neuronal mechanisms behind it have not yet been elucidated. Aiming to reveal potential neural control system compensation for external disturbances, we applied an external orientation perturbation while monkeys performed an orientation reaching task and simultaneously recorded the neural activity in the primary motor cortex (M1). We found that a subpopulation of neurons in the primary motor cortex specially created a time-locked activity in response to a “go” signal in the adaptation phase of the impending orientation perturbation and did not react to a “go” signal under the normal task condition without perturbation. Such neuronal activity was amplified as the alteration was processed and retained in the extinction phase; then, the activity gradually faded out. The increases in activity during the adaptation to the orientation perturbation may prepare the system for the impending response. Our work provides important evidence for understanding how the motor cortex responds to external perturbations and should advance research about the neurophysiological mechanisms underlying motor learning and adaptation

Image Reconstruction with Multiscale Interest Points Based on a Conditional Generative Adversarial Network

A new image reconstruction (IR) algorithm from multiscale interest points in the discrete wavelet transform (DWT) domain was proposed based on a modified conditional generative adversarial network (CGAN). The proposed IR-DWT-CGAN model generally integrated a DWT module, an interest point extraction module, an inverse DWT module, and a CGAN. First, the image was transformed using the DWT to provide multi-resolution wavelet analysis. Then, the multiscale maxima points were treated as interest points and extracted in the DWT domain. The generator was a U-net structure to reconstruct the original image from a very coarse version of the image obtained from the inverse DWT of the interest points. The discriminator network was a fully convolutional network, which was used to distinguish the restored image from the real one. The experimental results on three public datasets showed that the proposed IR-DWT-CGAN model had an average increase of 2.9% in the mean structural similarity, an average decrease of 39.6% in the relative dimensionless global error in synthesis, and an average decrease of 48% in the root-mean-square error compared with several other state-of-the-art methods. Therefore, the proposed IR-DWT-CGAN model is feasible and effective for image reconstruction with multiscale interest points

Image Reconstruction from Multiscale Singular Points Based on the Dual-Tree Complex Wavelet Transform

International audienceThe representation of an image with several multiscale singular points has been the main concern in image processing. Based on the dual-tree complex wavelet transform (DT-CWT), a new image reconstruction (IR) algorithm from multiscale singular points is proposed. First, the image was transformed by DT-CWT, which provided multiresolution wavelet analysis. Then, accurate multiscale singular points for IR were detected in the DT-CWT domain due to the shift invariance and directional selectivity properties of DT-CWT. Finally, the images were reconstructed from the phases and magnitudes of the multiscale singular points by alternating orthogonal projections between the CT-DWT space and its affine space. Theoretical analysis and experimental results show that the proposed IR algorithm is feasible, efficient, and offers a certain degree of denoising. Furthermore, the proposed IR algorithm outperforms other classical IR algorithms in terms of performance metrics such as peak signal-to-noise ratio, mean squared error, and structural similarity

Image Reconstruction with Multiscale Interest Points Based on a Conditional Generative Adversarial Network

International audienceA new image reconstruction (IR) algorithm from multiscale interest points in the discrete wavelet transform (DWT) domain was proposed based on a modified conditional generative adversarial network (CGAN). The proposed IR-DWT-CGAN model generally integrated a DWT module, an interest point extraction module, an inverse DWT module, and a CGAN. First, the image was transformed using the DWT to provide multi-resolution wavelet analysis. Then, the multiscale maxima points were treated as interest points and extracted in the DWT domain. The generator was a U-net structure to reconstruct the original image from a very coarse version of the image obtained from the inverse DWT of the interest points. The discriminator network was a fully convolutional network, which was used to distinguish the restored image from the real one. The experimental results on three public datasets showed that the proposed IR-DWT-CGAN model had an average increase of 2.9% in the mean structural similarity, an average decrease of 39.6% in the relative dimensionless global error in synthesis, and an average decrease of 48% in the root-mean-square error compared with several other state-of-the-art methods. Therefore, the proposed IR-DWT-CGAN model is feasible and effective for image reconstruction with multiscale interest points