scholarly journals A Novel Image Authentication with Tamper Localization and Self-Recovery in Encrypted Domain Based on Compressive Sensing

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Rui Zhang ◽  
Di Xiao ◽  
Yanting Chang
Keyword(s):  
Compressive Sensing ◽  
High Frequency ◽  
Low Frequency ◽  
Image Transmission ◽  
Image Authentication ◽  
Tamper Detection ◽  
Discrete Wavelet ◽  
Original Image ◽  
High Frequency Part ◽  
Tamper Recovery

This paper proposes a novel tamper detection, localization, and recovery scheme for encrypted images with Discrete Wavelet Transformation (DWT) and Compressive Sensing (CS). The original image is first transformed into DWT domain and divided into important part, that is, low-frequency part, and unimportant part, that is, high-frequency part. For low-frequency part contains the main information of image, traditional chaotic encryption is employed. Then, high-frequency part is encrypted with CS to vacate space for watermark. The scheme takes the processed original image content as watermark, from which the characteristic digest values are generated. Comparing with the existing image authentication algorithms, the proposed scheme can realize not only tamper detection and localization but also tamper recovery. Moreover, tamper recovery is based on block division and the recovery accuracy varies with the contents that are possibly tampered. If either the watermark or low-frequency part is tampered, the recovery accuracy is 100%. The experimental results show that the scheme can not only distinguish the type of tamper and find the tampered blocks but also recover the main information of the original image. With great robustness and security, the scheme can adequately meet the need of secure image transmission under unreliable conditions.

scholarly journals Novel Meaningful Image Encryption Based on Block Compressive Sensing

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Chen Pan ◽  
Guodong Ye ◽  
Xiaoling Huang ◽  
Junwei Zhou
Keyword(s):  
Image Compression ◽  
Compressive Sensing ◽  
Image Encryption ◽  
High Frequency ◽  
Low Frequency ◽  
Encryption Algorithm ◽  
Discrete Wavelet ◽  
Secret Image ◽  
Plain Image ◽  
Carrier Image

This paper proposes a new image compression-encryption algorithm based on a meaningful image encryption framework. In block compressed sensing, the plain image is divided into blocks, and subsequently, each block is rendered sparse. The zigzag scrambling method is used to scramble pixel positions in all the blocks, and subsequently, dimension reduction is undertaken via compressive sensing. To ensure the robustness and security of our algorithm and the convenience of subsequent embedding operations, each block is merged, quantized, and disturbed again to obtain the secret image. In particular, landscape paintings have a characteristic hazy beauty, and secret images can be camouflaged in them to some extent. For this reason, in this paper, a landscape painting is selected as the carrier image. After a 2-level discrete wavelet transform (DWT) of the carrier image, the low-frequency and high-frequency coefficients obtained are further subjected to a discrete cosine transform (DCT). The DCT is simultaneously applied to the secret image as well to split it. Next, it is embedded into the DCT coefficients of the low-frequency and high-frequency components, respectively. Finally, the encrypted image is obtained. The experimental results show that, under the same compression ratio, the proposed image compression-encryption algorithm has better reconstruction effect, stronger security and imperceptibility, lower computational complexity, shorter time consumption, and lesser storage space requirements than the existing ones.

scholarly journals An Iterative Hybrid Harmonics Detection Method Based on Discrete Wavelet Transform and Bartlett–Hann Window

2020 ◽  
Vol 10 (11) ◽  
pp. 3922 ◽  
Author(s):  
Guishuo Wang ◽  
Xiaoli Wang ◽  
Chen Zhao
Keyword(s):  
Wavelet Transform ◽  
Steady State ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Detection Method ◽  
Low Frequency ◽  
Discrete Wavelet ◽  
Detection Accuracy ◽  
Harmonic Detection ◽  
High Frequency Part

The current signal harmonic detection method(s) cannot reduce the errors in the analysis and extraction of mixed harmonics in the power grid. This paper designs a harmonic detection method based on discrete Fourier transform (DFT) and discrete wavelet transform (DWT) using Bartlett–Hann window function. It improves the detection accuracy of the existing methods in the low frequency steady-state part. In addition, it also separates the steady harmonics from the attenuation harmonics of the high frequency part. Simulation results show that the proposed harmonic detection method improves the detection accuracy of the steady-state part by 1.5175% compared to the existing method. The average value of low frequency steady-state amplitude detection of the proposed method is about 95.3375%. At the same time, the individual harmonic components of the signal are accurately detected and recovered in the high frequency part, and separation of the steady-state harmonics and the attenuated harmonics is achieved. This method is beneficial to improve the ability of harmonic analysis in the power grid.

Tamper Detection and Self-Recovery Watermarking Scheme Based on DWT-SVD

2011 ◽  
Vol 225-226 ◽  
pp. 614-618
Author(s):  
Yu Ping Hu ◽  
Jun Zhang ◽  
Hua Yin ◽  
Yi Chun Liu ◽  
Ying Hong Liang
Keyword(s):  
High Frequency ◽  
Wavelet Transformation ◽  
Low Frequency ◽  
Singular Value ◽  
Tamper Detection ◽  
Intentional Content ◽  
Discrete Wavelet ◽  
Watermark Embedding ◽  
Value Decomposition ◽  
Watermarking Scheme

This paper studied the image tamper detection and recovery watermarking scheme based on the discrete wavelet transformation(DWT) and the singular value decomposition (SVD).By the property of DWT and SVD , we design two watermarks which are embedded into the high-frequency bands of the DWT domain.One watermark is from the U component of the SVD domain and used for detecting the intentional content modification and indicating the modified location, and another watermark is from the low-frequency of DWT and used for recovering the image. The watermark generation and watermark embedding are disposed in the image itself. The experimental results show that the proposed scheme can resist the mild modifications of digital image and be able to detect and recovery the malicious modifications precisely.

scholarly journals Multi-focus Image Fusion Using an Effective Discrete Wavelet Transform Based Algorithm

2014 ◽  
Vol 14 (2) ◽  
pp. 102-108 ◽  
Author(s):  
Yong Yang ◽  
Shuying Huang ◽  
Junfeng Gao ◽  
Zhongsheng Qian
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Low Frequency ◽  
Objective Evaluation ◽  
Discrete Wavelet ◽  
Evaluation Indexes ◽  
Fused Image ◽  
Focus Image

Abstract In this paper, by considering the main objective of multi-focus image fusion and the physical meaning of wavelet coefficients, a discrete wavelet transform (DWT) based fusion technique with a novel coefficients selection algorithm is presented. After the source images are decomposed by DWT, two different window-based fusion rules are separately employed to combine the low frequency and high frequency coefficients. In the method, the coefficients in the low frequency domain with maximum sharpness focus measure are selected as coefficients of the fused image, and a maximum neighboring energy based fusion scheme is proposed to select high frequency sub-bands coefficients. In order to guarantee the homogeneity of the resultant fused image, a consistency verification procedure is applied to the combined coefficients. The performance assessment of the proposed method was conducted in both synthetic and real multi-focus images. Experimental results demonstrate that the proposed method can achieve better visual quality and objective evaluation indexes than several existing fusion methods, thus being an effective multi-focus image fusion method.

Non-uniform dependence on initial data for the generalized Degasperis–Procesi equation on the line

2016 ◽  
Vol 27 (03) ◽  
pp. 1650026
Author(s):  
Yanggeng Fu ◽  
Zanping Yu ◽  
Jianhe Shen
Keyword(s):  
Initial Data ◽  
Sobolev Spaces ◽  
High Frequency ◽  
Low Frequency ◽  
Approximate Solutions ◽  
Solution Map ◽  
High Frequency Part ◽  
Uniformly Continuous

In this paper, we show that the solution map of the generalized Degasperis–Procesi (gDP) equation is not uniformly continuous in Sobolev spaces [Formula: see text] for [Formula: see text]. Our proof is based on the estimates for the actual solutions and the approximate solutions, which consist of a low frequency and a high frequency part. It also exploits the fact that the gDP equation conserves a quantity which is equivalent to the [Formula: see text] norm.

scholarly journals Recognition of Emotional States using Multiscale Information Analysis of High Frequency EEG Oscillations

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 609 ◽  
Author(s):  
Gao ◽  
Cui ◽  
Wan ◽  
Gu
Keyword(s):  
Emotion Recognition ◽  
High Frequency ◽  
Low Frequency ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Emotional States ◽  
Circumplex Model ◽  
Information Analysis ◽  
Eeg Signals ◽  
High Frequency Oscillations

Exploring the manifestation of emotion in electroencephalogram (EEG) signals is helpful for improving the accuracy of emotion recognition. This paper introduced the novel features based on the multiscale information analysis (MIA) of EEG signals for distinguishing emotional states in four dimensions based on Russell's circumplex model. The algorithms were applied to extract features on the DEAP database, which included multiscale EEG complexity index in the time domain, and ensemble empirical mode decomposition enhanced energy and fuzzy entropy in the frequency domain. The support vector machine and cross validation method were applied to assess classification accuracy. The classification performance of MIA methods (accuracy = 62.01%, precision = 62.03%, recall/sensitivity = 60.51%, and specificity = 82.80%) was much higher than classical methods (accuracy = 43.98%, precision = 43.81%, recall/sensitivity = 41.86%, and specificity = 70.50%), which extracted features contain similar energy based on a discrete wavelet transform, fractal dimension, and sample entropy. In this study, we found that emotion recognition is more associated with high frequency oscillations (51–100Hz) of EEG signals rather than low frequency oscillations (0.3–49Hz), and the significance of the frontal and temporal regions are higher than other regions. Such information has predictive power and may provide more insights into analyzing the multiscale information of high frequency oscillations in EEG signals.

Dynamic relation between global Islamic and conventional sectoral stock and bonds indexes

2020 ◽  
Vol 07 (02) ◽  
pp. 2050006
Author(s):  
Sukriye Tuysuz
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Stock Index ◽  
High Frequency Part ◽  
Dynamic Relation ◽  
The Relationship

This paper examines the relationship between 10 Global sectoral conventional and Islamic assets. For each sector, a conventional, an Islamic stock index and a bond are retained. The analyzed relations are done by taking into account diverse investment horizons by using MODWT and GARCH-DCC-type models. Our results indicate that adding bond indexes into a portfolio composed with conventional stock or Islamic stock is efficient. As for the correlations between conventional and Islamic sectoral indexes, they depend on the sector. Relations between returns of securities are quite similar to the relations between high-frequency part of these series and are very volatile at low frequency.

Multisource Image Fusion Based on DWT and Simplified Pulse Coupled Neural Network

2013 ◽  
Vol 457-458 ◽  
pp. 736-740 ◽  
Author(s):  
Nian Yi Wang ◽  
Wei Lan Wang ◽  
Xiao Ran Guo
Keyword(s):  
Neural Network ◽  
Image Fusion ◽  
High Frequency ◽  
Low Frequency ◽  
Discrete Wavelet ◽  
Cortical Model ◽  
Fusion Algorithm ◽  
Global Coupling ◽  
Maximum Selection ◽  
Pulse Synchronization

In this paper, a new image fusion algorithm based on discrete wavelet transform (DWT) and spiking cortical model (SCM) is proposed. The multiscale decomposition and multi-resolution representation characteristics of DWT are associated with global coupling and pulse synchronization features of SCM. Two different fusion rules are used to fuse the low and high frequency sub-bands respectively. Maximum selection rule (MSR) is used to fuse low frequency coefficients. As to high frequency subband coefficients, spatial frequency (SF) is calculated and then imputed into SCM to motivate neural network. Experimental results demonstrate the effectiveness of the proposed fusion method.

scholarly journals Extracting super-resolution details directly from a diffraction-blurred image or part of its frequency spectrum

2019 ◽  
Author(s):  
Edward Y Sheffield
Keyword(s):  
High Frequency ◽  
Simulation Experiment ◽  
Fourier Spectrum ◽  
Low Frequency ◽  
Super Resolution ◽  
Diffraction Limit ◽  
Full Information ◽  
High Frequency Part ◽  
Blurred Image ◽  
Blurred Images

It is usually believed that the low frequency part of a signal’s Fourier spectrum represents its profile, while the high frequency part represents its details. Conventional light microscopes filter out the high frequency parts of image signals, so that people cannot see the details of the samples (objects being imaged) in the blurred images. However, we find that in a certain “resolvable condition”, a signal’s low frequency and high frequency parts not only represent profile and details respectively. Actually, any one of them also contains the full information (including both profile and details) of the sample’s structure. Therefore, for samples with spatial frequency beyond diffraction-limit, even if the image’s high frequency part is filtered out by the microscope, it is still possible to extract the full information from the low frequency part. On the basis of the above findings, we propose the technique of Deconvolution Super-resolution (DeSu-re), including two methods. One method extracts the full information of the sample’s structure directly from the diffraction-blurred image, while the other extracts it directly from part of the observed image’s spectrum (e.g., low frequency part). Both theoretical analysis and simulation experiment support the above findings, and also verify the effectiveness of the proposed methods.

scholarly journals Degraded Image Enhancement Using Dual-Domain-Adaptive Wavelet and Improved Fuzzy Transform

2020 ◽  
Author(s):  
Yuehua Huo ◽  
Weiqiang Fan ◽  
Xiaoyu Li
Keyword(s):  
Performance Evaluation ◽  
Image Enhancement ◽  
High Frequency ◽  
Low Frequency ◽  
Original Image ◽  
Fuzzy Transform ◽  
Adaptive Wavelet ◽  
Comprehensive Performance ◽  
Degraded Image ◽  
Dual Domain

Abstract A novel enhancement algorithm of degraded image based on dual-domain-adaptive wavelet and improved fuzzy transform is proposed, aiming at the problem of surveillance videos degradation caused by complex lighting conditions underground. The dual-domain filtering (DDF) is used to decompose the image into low-frequency sub-image and high-frequency sub-images. The contrast limited adaptive histogram enhancement (CLAHE) is used to adjust the overall brightness and contrast of the low-frequency sub-image. Discrete wavelet transform (DWT) is used to obtain low frequency sub-band (LFS) and high frequency sub-band (HFS). The wavelet shrinkage threshold method based on Bayesian estimation is used to calculate the wavelet threshold corresponding to the HFS at different scales. A Garrate threshold function that introduces adaptive adjustment factor and enhancement coefficient is designed to adaptively de-noise and enhance the HFS coefficients corresponding to wavelet thresholds at different scales. Meanwhile, the gamma function is used to realize the correction of the LFS coefficients. The constructed PAL fuzzy enhancement operator is used to perform contrast enhancement and highlight area suppression on the reconstructed image to obtain an enhanced image. The proposed algorithm is evaluated by subjective vision and objective indicators. The experimental results show that the proposed algorithm can significantly improve the overall brightness and contrast of the original image, suppress noise of dust & spray, enhance the image details and improve the visual effect of the original image. Compared with the images enhanced by the STFE, GTFE, CLAHE, SSR, MSR, DGR, and MSWT algorithms, the comprehensive performance evaluation indicators of the images enhanced by the proposed algorithm are increased by 312.50%, 34.69%, 53.49%, 22.22%, 32.00%, 10.00%, 60.98%, 3.13%, respectively. At the same time, comprehensive performance evaluation indicator of the enhance image and the robustness is the best, which is more suitable for image enhancement in different mine environments.

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