High performance reversible data hiding scheme through multilevel histogram modification in lifting integer wavelet transform

Subburam, S.; Selvakumar, S.; Geetha, S.

doi:10.1007/s11042-017-4622-0

Cited by 43 publications

(6 citation statements)

References 38 publications

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“…Machine learning techniques can improve wireless network security, reduce congestion, and aid in physical layer authentication and error detection [17][18][19]. ML techniques also help analyze WSN packets and identify problematic nodes [26][27][28][29].…”

Section: Literature Surveymentioning

confidence: 99%

A Novel Model for Network Intrusion Detection by Using Statistical Learning Approach

Jeevaraj¹,

B.²,

M.³

et al. 2022

INDJCSE

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Network intrusion detection is the pressing need of every communication network. Network Intrusion Detection Systems (NIDSes) play an important role in security operations to detect and defend against cyberattacks. As artificial intelligence (AI)-powered NIDSes are adaptive to various kinds of attacks by exploring the knowledge presented in the data, they are in high demand to treat the cyber-attacks nowadays with increasing diversity and intensity. This research works finds that the efficient learning to overcome these issues. The NBMU model performs well and it holds an accuracy 89.44%. The NBM shows worst output which as 65.80%. The NBMU and NB models perform well and it have same precision value 0.89. The NBM model has poor performance which as 0.66. The NBMU and NB models perform well and it have same recall value 0.89. The NBM model has poor performance which as 0.66. The NBMU and NB models perform well and it have same F-Measure value 0.89. The NBM model has poor performance which as 0.64. The NBMU has ROC level 0.90 which as highest performance compare with other models. The NBM model has shown the worst outcome which as 0.59.The NBMU has highest PRC level 0.90. The NBM model has not satisfactory which as 0.59 of PRC value. The NBMU and NB has the highest MCC 0.88. The NBM model has poor performance which as 0.36 of MCC value. The NBMU and NB has same kappa 0.88. The NBM model has poor performance which as 0.32 of kappa value. The NBMU and NB has the least MAE 0.09. The NBM model has poor performance which as 0.32 of MAE value. The NBM has poor performance which as 0.89.The NB has the least RAE 10.52%. The NBU modes has poor performance as well same RAE which as 116.64%. The NBU modes has poor performance as well same RRSE which as 132.51%.The NBMU has poor performance for making its model which as 20.02 seconds.

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Section: Literature Surveymentioning

confidence: 99%

A Novel Model for Network Intrusion Detection by Using Statistical Learning Approach

Jeevaraj¹,

B.²,

M.³

et al. 2022

INDJCSE

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“…Light-based sensing: The goal is to convert light signals into electrical impulses using photo detectors. [24] The conversion of analog electrical signals to digital signals is subsequently performed using an ADC. Two light-based sensing systems for identifying human activity have been proposed by researchers.…”

Section: Literature Surveymentioning

confidence: 99%

A Second Order Inductive Deduction Approach for Identification of Room Occupancy by Wireless Sensors

Jeevaraj¹,

B.²,

M.³

et al. 2022

INDJCSE

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Occupancy detection is crucial in many smart building applications, including reducing building energy consumption by managing heating, ventilation, and air conditioning systems, monitoring systems, and lighting system management, tracking patients in hospitals for medical issues, advertising to shoppers in malls, and search and rescue missions. The global positioning system is most frequently employed as a localization technique, yet it is incredibly imprecise when used indoors. The interior environment is challenging to manage because, in addition to the signal loss, privacy is a significant issue. Indoor tracking and wireless sensor network sensor localization share many similarities. Machine Learning helps to overcome the mentioned issues. This research works finds that the Attribute Selected Classifier with Naïve Bayes Updateable of second order ensemble model gives highest performance which as accuracy level 86.69%%, kappa statistic value 0.68, precision value 0.87, recall value 0.87, F-Measure value 0.86, Matthews connection coefficient value 0.68. The Attribute Selected Classifier with Naïve Bayes Updateable of second order ensemble model gives highest performance which as ROC value 0.89 and PRC value 0.89, MAE value 0.15, RMSE value 0.40, RAE value 47.32%, RRSE value 90.11% and it takes time consumption as 0.09 seconds to build a model which is produced an optimal results based on their performance compare with other models. This Attribute Selected Classifiers with Naïve Bayes Updateable model is performing well compare with other models.

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“…Huang et al [38] proposed a reversible watermarking algorithm for a three-dimensional (3-D) model based on histogram shifting, in which the distance between the adjacent vertices of the high-resolution 3-D model is used to construct the difference histogram, which gives rise to higher peak values. Subburam et al [39] performed an integer lifting wavelet transform on the image and constructed difference histograms by scanning the transform coefficient of each sub-band. The scanning sequence was stored as the key to improve security, and watermarks were then embedded through multilevel histogram modification.…”

Section: Reversible Watermarking Based On Histogram Shiftingmentioning

confidence: 99%

A Reversible Watermarking Scheme for Vector Maps Based on Multilevel Histogram Modification

2018

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To protect the security of vector maps, we propose a novel reversible watermarking scheme for vector maps based on a multilevel histogram modification. First, a difference histogram is constructed using the correlations of adjacent coordinates, and the histogram is divided into continuous regions and discontinuous regions by combining the characteristics of vector map data. Second, the histogram bins that require modification are determined in the continuous regions through the optimal peak value, and the peak values are chosen from the flanking discontinuous regions in both directions; the watermarks are embedded by adopting the multilevel histogram modification strategy. The watermark extraction process is the reverse of the embedding process, and after completing the watermark extraction, the carrier data can be recovered losslessly. The experimental results show that the proposed algorithm has good invisibility and is completely reversible. Compared with similar algorithms reported previously, it achieves higher watermark embedding capacity under the same embedding distortion with lower complexity, thereby having a higher application value.

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High performance reversible data hiding scheme through multilevel histogram modification in lifting integer wavelet transform

Cited by 43 publications

References 38 publications

A Novel Model for Network Intrusion Detection by Using Statistical Learning Approach

A Novel Model for Network Intrusion Detection by Using Statistical Learning Approach

A Second Order Inductive Deduction Approach for Identification of Room Occupancy by Wireless Sensors

A Reversible Watermarking Scheme for Vector Maps Based on Multilevel Histogram Modification

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