Reversible Fragile Watermarking based on Difference Expansion Using Manhattan Distances for 2D Vector Map

Neyman, Shelvie Nidya; Sitohang, Benhard; Sutisna, Sobar

doi:10.1016/j.protcy.2013.12.236

Cited by 14 publications

(7 citation statements)

References 8 publications

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“…There are many approaches, like Manhattan distance [14], Minkowski distance [15], Euclidean distance [16] etc., to measure the distance between two data vector [3], [4]. In this work, the sum of squared Euclidean (SSE) distance between each data sample and the cluster center which data sample belongs is used as objective function to be minimized by algorithms.…”

Section: The Clustering Problemmentioning

confidence: 99%

Grey Wolf Optimizer (GWO) Algorithm to Solve the Partitional Clustering Problem

Karakoyun

Inan²,

Akto

2019

ijisae

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The clustering which is an unsupervised classification method is very important for data processing applications. The main purpose of the clustering is to separate the data samples into different groups by using the similarity (or dissimilarity) between data samples. There are many conventional and heuristic algorithms which are used for the clustering problem. Nevertheless, in last years, it is seen that many new techniques are proposed and improved to solve the clustering problem. In this paper, grey wolf optimization (GWO) algorithm which is modelled according to the social behavior of grey wolves is applied to partition the data samples by searching the optimal center of the clusters. The clustering performance of the GWO is compared with the performances of the three clustering algorithms: k-means, kmedoids and fuzzy c-means algorithms. The experiments show that the GWO algorithm has generally better results than the other clustering algorithms and can be alternatively applied on the clustering problem.

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Section: The Clustering Problemmentioning

confidence: 99%

Grey Wolf Optimizer (GWO) Algorithm to Solve the Partitional Clustering Problem

Karakoyun

Inan²,

Akto

2019

ijisae

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“…In order to guarantee the high quality of the watermarked 2D vector map, it is crucial to restrict as much as possible the distortion causing by the embedding process. This must be restricted by the map's precision tolerance [15]. Euclidean distances can be employed here to work out the extent of the distortions (Eq.…”

Section: )mentioning

confidence: 99%

“…Once the outlined process is complete, then the original difference for each unit will be obtained. When collaborating these with the integer-mean , it is then possible to work out the original coordinates of each unit by applying equations (13) to (15). To work out the watermark W' through the given method, the process above must be used.…”

Section: )mentioning

confidence: 99%

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Fragile Watermarking based on Linear Cellular Automata using Manhattan Distances for 2D Vector Map

AL-ardhi¹,

Thayananthan²,

Basuhail³

2019

IJACSA

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There has been a growing demand for publishing maps in secure digital format, since this ensures the integrity of data. This had lead us to put forward a method of detecting and locating modification data that is extremely accurate and simultaneously guarantees that the exact original content is recovered. More precisely, this method relies on a fragile watermarking algorithm that is developed in accordance with a frequency manner and, for every spatial feature, it can embed hidden data in 2D vector maps. The current paper proposes a frequency data-hiding scheme, which will be examined in accordance with Linear Cellular Automata Transform using Manhattan distances. Various invertible integer mappings are applied in order to find out the Manhattan distances from coordinates. To begin with, the original map is transformed into LCA, after which the watermark insertion process is carried out to transform the coefficient of the transformation result frequency into LSB. Lastly, a watermarked map is created by applying the inverse LCA transform, meaning that a LCAtransformed map is produced. Findings indicate that the suggested method is effective since in terms of invisibility and the capacity to allow for modifications. The methods also allow the detection of modification data, the addition and removal of some features, and enable the exact original content from the 2D vector map to be included.

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“…However, the data structure of vector data is complex, including geometric information, attribute information, and topology information, which has four basic features of spatial, attribute, temporal, and scale, which increases the difficulty of vector data digital watermarking research. Currently, most scholars use the spatial features of vector data to study vector data digital watermarking technology due to the inapplicability of the attribute, temporal, and scale features of vector data [11][12][13][14][15][16][17][18][19]. Thus, the robustness of the watermarking algorithm after the spatial features of vector data is subjected to different geometric attack methods must be considered in the research of vector data digital watermarking algorithms.…”

mentioning

confidence: 99%

A Novel Infringement Detection Method for GIS Vector Data

Tang

Zhang

Jing

et al. 2019

IJGI

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There is undoubtedly a groundswell of support for the concept of geographic data sharing with the rapid development and wide-ranging application of geographic information science. However, copyright protection and infringement detection in the process of geographic data sharing has always been an important issue that needs to be addressed urgently. In this paper, we present a novel infringement detection method for GIS vector data to compensate for the shortcomings of vector data digital watermarking technology in infringement detection. The method determines whether infringement exists by the duplication degree between the original data and the vector data to be detected in three features including feature features, included angle features and vertex features which gets by using the spatial information of vector data to perform the feature matching based on GeoJSON format data. The experimental results indicate that the proposed algorithm can effectively resist common geometric attacks, such as interpolation attack, deletion attack, similarity transformation attack, feature order scrambling attack, and feature simplification attack, on vector data, which proves that the proposed algorithm has excellent robustness and meets the requirements of practical application. but there is growing demand for geographic data in society. This forms a contradiction, and the issue of geographic data sharing needs to be resolved as soon as possible.In order to promote the sharing of geographic data, some international organizations and countries have set up systems and platforms [3][4][5] dedicated to the sharing of geographic data, and there have also been a number of crowd-sourced geographic information platforms [6] established spontaneously. The emergence of these platforms has provided good geographic data support for research, education, and development of the geographic information discipline, but still fails to resolve the above contradiction. there is an urgent demand for a technology that can protect the copyright of geographic data effectively in the case of imperfect laws and regulations.At present, encryption control technology and digital watermarking technology are the main means of geographic data copyright protection [8]. Among them, encryption control technology combines computer hardware, computer software, and geographic data to control the reading and writing of geographic data at the bottom of the computer system. It can prevent users from reading, abusing, and tampering with unauthorized geographic data on unauthorized computers, and can prevent illegal acts such as infringement and disclosure of geographic data in advance. However, this technology is more suitable for the protection of classified data than the public-oriented geographic data sharing process because it is demanding on software and hardware. Digital watermarking technology is an after-the-fact pursuit technique [9], which embeds watermark information (such as a copyright information identification image) into geographic data, and the geogra...

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Reversible Fragile Watermarking based on Difference Expansion Using Manhattan Distances for 2D Vector Map

Cited by 14 publications

References 8 publications

Grey Wolf Optimizer (GWO) Algorithm to Solve the Partitional Clustering Problem

Grey Wolf Optimizer (GWO) Algorithm to Solve the Partitional Clustering Problem

Fragile Watermarking based on Linear Cellular Automata using Manhattan Distances for 2D Vector Map

A Novel Infringement Detection Method for GIS Vector Data

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