scholarly journals A Logarithmic Quantization-Based Image Watermarking Using Information Entropy in the Wavelet Domain

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 945 ◽  
Author(s):  
Jinhua Liu ◽  
Shan Wu ◽  
Xinye Xu
Keyword(s):  
Image Watermarking ◽  
Additive White Gaussian Noise ◽  
The Novel ◽  
Method Performance ◽  
High Entropy ◽  
Watermark Embedding ◽  
Uniform Quantization ◽  
Novel Method ◽  
Perceptual Characteristics ◽  
Host Signal

Conventional quantization-based watermarking may be easily estimated by averaging on a set of watermarked signals via uniform quantization approach. Moreover, the conventional quantization-based method neglects the visual perceptual characteristics of the host signal; thus, the perceptible distortions would be introduced in some parts of host signal. In this paper, inspired by the Watson’s entropy masking model and logarithmic quantization index modulation (LQIM), a logarithmic quantization-based image watermarking method is developed by using the wavelet transform. Furthermore, the novel method improves the robustness of watermarking based on a logarithmic quantization strategy, which embeds the watermark data into the image blocks with high entropy value. The main significance of this work is that the trade-off between invisibility and robustness is simply addressed by using the logarithmic quantizaiton approach, which applies the entropy masking model and distortion-compensated scheme to develop a watermark embedding method. In this manner, the optimal quantization parameter obtained by minimizing the quantization distortion function effectively controls the watermark strength. In terms of watermark decoding, we model the wavelet coefficients of image by the generalized Gaussian distribution (GGD) and calculate the bit error probability of proposed method. Performance of the proposed method is analyzed and verified by simulation on real images. Experimental results demonstrate that the proposed method has the advantages of imperceptibility and strong robustness against attacks covering JPEG compression, additive white Gaussian noise (AWGN), Gaussian filtering, Salt&Peppers noise, scaling and rotation attack, etc.

scholarly journals Spatial-Perceptual Embedding with Robust Just Noticeable Difference Model for Color Image Watermarking

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1506 ◽  
Author(s):  
Kai Zhou ◽  
Yunming Zhang ◽  
Jing Li ◽  
Yantong Zhan ◽  
Wenbo Wan
Keyword(s):  
Color Image ◽  
Image Watermarking ◽  
Human Vision ◽  
Just Noticeable Difference ◽  
Color Image Watermarking ◽  
Quantization Step ◽  
Dct Domain ◽  
Watermark Embedding ◽  
Robust Image ◽  
Perceptual Characteristics

In the robust image watermarking framework, watermarks are usually embedded in the direct current (DC) coefficients in discrete cosine transform (DCT) domain, since the DC coefficients have a larger perceptual capacity than any alternating current (AC) coefficients. However, DC coefficients are also excluded from watermark embedding with the consideration of avoiding block artifacts in watermarked images. Studies on human vision suggest that perceptual characteristics can achieve better image fidelity. With this perspective, we propose a novel spatial–perceptual embedding for a color image watermarking algorithm that includes the robust just-noticeable difference (JND) guidance. The logarithmic transform function is used for quantization embedding. Meanwhile, an adaptive quantization step is modeled by incorporating the partial AC coefficients. The novelty and effectiveness of the proposed framework are supported by JND perceptual guidance for spatial pixels. Experiments validate that the proposed watermarking algorithm produces a significantly better performance.

Blind Detection of Additive Spread-Spectrum Watermarking in the Dual-Tree Complex Wavelet Transform Domain

2012 ◽  
pp. 53-65
Author(s):  
Roland Kwitt ◽  
Peter Meerwald ◽  
Andreas Uhl
Keyword(s):  
Wavelet Transform ◽  
Spread Spectrum ◽  
Image Watermarking ◽  
Perceptual Quality ◽  
Ratio Test ◽  
Complex Wavelet Transform ◽  
Detector Performance ◽  
Complex Wavelet ◽  
Perceptual Characteristics ◽  
Host Signal

In this paper, the authors adapt two blind detector structures for additive spread-spectrum image watermarking to the host signal characteristics of the Dual-Tree Complex Wavelet Transform (DT-CWT) domain coefficients. The research is motivated by the superior perceptual characteristics of the DT-CWT and its active use in watermarking. To improve the numerous existing watermarking schemes in which the host signal is modeled by a Gaussian distribution, the authors show that the Generalized Gaussian nature of Dual-Tree detail subband statistics can be exploited for better detector performance. This paper finds that the Rao detector is more practical than the likelihood-ratio test for their detection problem. The authors experimentally investigate the robustness of the proposed detectors under JPEG and JPEG2000 attacks and assess the perceptual quality of the watermarked images. The results demonstrate that their alterations allow significantly better blind watermark detection performance in the DT-CWT domain than the widely used linear-correlation detector. As only the detection side has to be modified, the proposed methods can be easily adopted in existing DT-CWT watermarking schemes.

scholarly journals Quantization-Based Image Watermarking by Using a Normalization Scheme in the Wavelet Domain

Information ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 194 ◽  
Author(s):  
Jinhua Liu ◽  
Qiu Tu ◽  
Xinye Xu
Keyword(s):  
Wavelet Coefficient ◽  
Image Watermarking ◽  
Probability Of Detection ◽  
Experimental Simulation ◽  
False Alarms ◽  
Step Size ◽  
Image Region ◽  
Quantization Step ◽  
High Entropy ◽  
Watermark Embedding

To improve the invisibility and robustness of quantization-based image watermarking algorithms, we developed an improved quantization image watermarking method based on the wavelet transform and normalization strategy used in this study. In the process of watermark encoding, the sorting strategy of wavelet coefficients is used to calculate the quantization step size. Its robustness lies in the normalization-based watermark embedding and the control of its amount of modification on each wavelet coefficient by utilizing the proper quantization parameter in a high entropy image region. In watermark detection, the original unmarked image is not required, and the probability of false alarms and the probability of detection are discussed through experimental simulation. Experimental results show the effectiveness of the proposed watermarking. Furthermore, the proposed method has stronger robustness than the alternative quantization-based watermarking algorithm.

scholarly journals Complex Wavelet-Based Image Watermarking with the Human Visual Saliency Model

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1462 ◽  
Author(s):  
Jinhua Liu ◽  
Yunbo Rao ◽  
Yuanyuan Huang
Keyword(s):  
Image Watermarking ◽  
Similarity Index ◽  
Low Frequency ◽  
Visual Saliency ◽  
Structural Similarity ◽  
High Entropy ◽  
Saliency Model ◽  
Watermark Embedding ◽  
Complex Wavelet ◽  
Visual Saliency Model

Imperceptibility and robustness are the two complementary, but fundamental requirements of any digital image watermarking method. To improve the invisibility and robustness of multiplicative image watermarking, a complex wavelet based watermarking algorithm is proposed by using the human visual texture masking and visual saliency model. First, image blocks with high entropy are selected as the watermark embedding space to achieve imperceptibility. Then, an adaptive multiplicative watermark embedding strength factor is designed by utilizing texture masking and visual saliency to enhance robustness. Furthermore, the complex wavelet coefficients of the low frequency sub-band are modeled by a Gaussian distribution, and a watermark decoding method is proposed based on the maximum likelihood criterion. Finally, the effectiveness of the watermarking is validated by using the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) through experiments. Simulation results demonstrate the invisibility of the proposed method and its strong robustness against various attacks, including additive noise, image filtering, JPEG compression, amplitude scaling, rotation attack, and combinational attack.

A novel method for the determination of linting propensity of paper

TAPPI Journal ◽  
2012 ◽  
Vol 11 (10) ◽  
pp. 9-17
Author(s):  
ALESSANDRA GERLI ◽  
LEENDERT C. EIGENBROOD
Keyword(s):  
Ash Content ◽  
The Novel ◽  
Offset Printing ◽  
Excellent Correlation ◽  
End Conditions ◽  
Bottom Side ◽  
Novel Method ◽  
Total Fraction

A novel method was developed for the determination of linting propensity of paper based on printing with an IGT printability tester and image analysis of the printed strips. On average, the total fraction of the surface removed as lint during printing is 0.01%-0.1%. This value is lower than those reported in most laboratory printing tests, and more representative of commercial offset printing applications. Newsprint paper produced on a roll/blade former machine was evaluated for linting propensity using the novel method and also printed on a commercial coldset offset press. Laboratory and commercial printing results matched well, showing that linting was higher for the bottom side of paper than for the top side, and that linting could be reduced on both sides by application of a dry-strength additive. In a second case study, varying wet-end conditions were used on a hybrid former machine to produce four paper reels, with the goal of matching the low linting propensity of the paper produced on a machine with gap former configuration. We found that the retention program, by improving fiber fines retention, substantially reduced the linting propensity of the paper produced on the hybrid former machine. The papers were also printed on a commercial coldset offset press. An excellent correlation was found between the total lint area removed from the bottom side of the paper samples during laboratory printing and lint collected on halftone areas of the first upper printing unit after 45000 copies. Finally, the method was applied to determine the linting propensity of highly filled supercalendered paper produced on a hybrid former machine. In this case, the linting propensity of the bottom side of paper correlated with its ash content.

scholarly journals REMAXINT: a two-mode clustering-based method for statistical inference on two-way interaction

2021 ◽  
Author(s):  
Zaheer Ahmed ◽  
Alberto Cassese ◽  
Gerard van Breukelen ◽  
Jan Schepers
Keyword(s):  
Null Hypothesis ◽  
Type I Error ◽  
Type I ◽  
The Novel ◽  
Simulation Studies ◽  
Test Statistic ◽  
Clustering Model ◽  
Novel Method ◽  
Main Effects ◽  
Empirical Case Studies

AbstractWe present a novel method, REMAXINT, that captures the gist of two-way interaction in row by column (i.e., two-mode) data, with one observation per cell. REMAXINT is a probabilistic two-mode clustering model that yields two-mode partitions with maximal interaction between row and column clusters. For estimation of the parameters of REMAXINT, we maximize a conditional classification likelihood in which the random row (or column) main effects are conditioned out. For testing the null hypothesis of no interaction between row and column clusters, we propose a $$max-F$$ m a x - F test statistic and discuss its properties. We develop a Monte Carlo approach to obtain its sampling distribution under the null hypothesis. We evaluate the performance of the method through simulation studies. Specifically, for selected values of data size and (true) numbers of clusters, we obtain critical values of the $$max-F$$ m a x - F statistic, determine empirical Type I error rate of the proposed inferential procedure and study its power to reject the null hypothesis. Next, we show that the novel method is useful in a variety of applications by presenting two empirical case studies and end with some concluding remarks.

scholarly journals Interrogating the Egypto-Sudanic Arabic Connection

Languages ◽  
2021 ◽  
Vol 6 (3) ◽  
pp. 123
Author(s):  
Thomas A. Leddy-Cecere
Keyword(s):  
The Novel ◽  
Present Contribution ◽  
Phonological Processes ◽  
Verbal Inflection ◽  
Segmental Phonology ◽  
Language Data ◽  
History Of ◽  
Novel Method ◽  
Dialect Classification ◽  
Arabic Speaking

The Arabic dialectology literature repeatedly asserts the existence of a macro-level classificatory relationship binding the Arabic speech varieties of the combined Egypto-Sudanic area. This proposal, though oft-encountered, has not previously been formulated in reference to extensive linguistic criteria, but is instead framed primarily on the nonlinguistic premise of historical demographic and genealogical relationships joining the Arabic-speaking communities of the region. The present contribution provides a linguistically based evaluation of this proposed dialectal grouping, to assess whether the postulated dialectal unity is meaningfully borne out by available language data. Isoglosses from the domains of segmental phonology, phonological processes, pronominal morphology, verbal inflection, and syntax are analyzed across six dialects representing Arabic speech in the region. These are shown to offer minimal support for a unified Egypto-Sudanic dialect classification, but instead to indicate a significant north–south differentiation within the sample—a finding further qualified via application of the novel method of Historical Glottometry developed by François and Kalyan. The investigation concludes with reflection on the implications of these results on the understandings of the correspondence between linguistic and human genealogical relationships in the history of Arabic and in dialectological practice more broadly.

scholarly journals Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering

2021 ◽  
Vol 13 (9) ◽  
pp. 4648
Author(s):  
Rana Muhammad Adnan ◽  
Kulwinder Singh Parmar ◽  
Salim Heddam ◽  
Shamsuddin Shahid ◽  
Ozgur Kisi
Keyword(s):  
Clustering Algorithm ◽  
Suspended Sediments ◽  
Ecological Impacts ◽  
Accurate Estimation ◽  
Data Sets ◽  
The Novel ◽  
Neuro Fuzzy ◽  
Adaptive Regression ◽  
Novel Method ◽  
Model Training

The accurate estimation of suspended sediments (SSs) carries significance in determining the volume of dam storage, river carrying capacity, pollution susceptibility, soil erosion potential, aquatic ecological impacts, and the design and operation of hydraulic structures. The presented study proposes a new method for accurately estimating daily SSs using antecedent discharge and sediment information. The novel method is developed by hybridizing the multivariate adaptive regression spline (MARS) and the Kmeans clustering algorithm (MARS–KM). The proposed method’s efficacy is established by comparing its performance with the adaptive neuro-fuzzy system (ANFIS), MARS, and M5 tree (M5Tree) models in predicting SSs at two stations situated on the Yangtze River of China, according to the three assessment measurements, RMSE, MAE, and NSE. Two modeling scenarios are employed; data are divided into 50–50% for model training and testing in the first scenario, and the training and test data sets are swapped in the second scenario. In Guangyuan Station, the MARS–KM showed a performance improvement compared to ANFIS, MARS, and M5Tree methods in term of RMSE by 39%, 30%, and 18% in the first scenario and by 24%, 22%, and 8% in the second scenario, respectively, while the improvement in RMSE of ANFIS, MARS, and M5Tree was 34%, 26%, and 27% in the first scenario and 7%, 16%, and 6% in the second scenario, respectively, at Beibei Station. Additionally, the MARS–KM models provided much more satisfactory estimates using only discharge values as inputs.

scholarly journals The Influence of Varying Fluorination Patterns on the Thermodynamics and Kinetics of Benzenesulfonamide Binding to Human Carbonic Anhydrase II

Biomolecules ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 509 ◽  
Author(s):  
Steffen Glöckner ◽  
Khang Ngo ◽  
Björn Wagner ◽  
Andreas Heine ◽  
Gerhard Klebe
Keyword(s):  
Carbonic Anhydrase ◽  
Carbonic Anhydrase Ii ◽  
The Novel ◽  
Binding Modes ◽  
X Ray ◽  
Structure Activity ◽  
Small Set ◽  
Novel Method ◽  
Human Carbonic Anhydrase ◽  
Kinetics Of

The fluorination of lead-like compounds is a common tool in medicinal chemistry to alter molecular properties in various ways and with different goals. We herein present a detailed study of the binding of fluorinated benzenesulfonamides to human Carbonic Anhydrase II by complementing macromolecular X-ray crystallographic observations with thermodynamic and kinetic data collected with the novel method of kinITC. Our findings comprise so far unknown alternative binding modes in the crystalline state for some of the investigated compounds as well as complex thermodynamic and kinetic structure-activity relationships. They suggest that fluorination of the benzenesulfonamide core is especially advantageous in one position with respect to the kinetic signatures of binding and that a higher degree of fluorination does not necessarily provide for a higher affinity or more favorable kinetic binding profiles. Lastly, we propose a relationship between the kinetics of binding and ligand acidity based on a small set of compounds with similar substitution patterns.

Sign in / Sign up

Export Citation Format

Share Document