This document summarizes and analyzes several existing image-based relational database watermarking techniques. It begins with background on database watermarking, including applications, classifications of techniques, desired characteristics of watermarked databases, and types of attacks. It then reviews four specific algorithms that embed image watermarks into database attributes. The algorithms are analyzed for robustness against different attacks like modification, deletion and addition of tuples. The document concludes various image-based techniques are effective for copyright protection and survive attacks while preserving data integrity.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online
Lsb hiding using random approach for image watermarkingeSAT Journals
Abstract A digital image watermarking is the process of embedding an image with a secondary parameter called watermark, without deterioration in the quality of image to provide copyright protection means to provide protection for intellectual property from illegal copying. In this paper the method of nested digital image watermarking is used that means a watermark inside another watermark embedded into the cover image that is the main image. Here the Randomized LSB hiding algorithm is used for embedding one image into another as it has lesser complexity and the approach is more robust to the variations in the type of image. The blowfish algorithm is used to encrypt the watermark image before embedding into the cover image. The concept of encryption of watermark image before get embedded into the main image is used here to increase the security of the watermark image. This is because the research work is mainly focus on to get the more secured watermark by improving and enhancing the embedding capacity. Key Words: Digital image Watermarking, Randomized LSB, Blowfish, Copyright Protection
This document discusses a randomized LSB hiding approach for nested digital image watermarking. It proposes encrypting one watermark image using Blowfish before embedding it into another watermark image using randomized LSB hiding. This nested watermark is then encrypted again using Blowfish before being embedded into the cover image for increased security. Randomized LSB hiding is used for embedding as it has lower complexity and is more robust than direct LSB hiding. The approach aims to improve security and embedding capacity for copyright protection of digital images.
IEEE 2014 DOTNET CLOUD COMPUTING PROJECTS Web image re ranking using query-sp...IEEEMEMTECHSTUDENTPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
The document summarizes recent advances in digital image watermarking techniques. It discusses how watermarking techniques can be classified based on the domain of embedding (spatial or transform), robustness to attacks (fragile or robust), and need for cover data during extraction (blind or non-blind). Specifically, it describes spatial domain techniques like least significant bit insertion and transform domain techniques using discrete Fourier transform, discrete cosine transform, and discrete wavelet transform. Transform techniques embed watermarks by modifying transform coefficients rather than pixel values.
This document describes a proposed cloud-based system for protecting multimedia content from unauthorized copying. The key components of the system include a crawler to download content from online sites, a novel signature method for creating fingerprints of 3D videos that captures depth signals, and a distributed matching engine that stores signatures and matches them against queries in a scalable way. The system was tested on over 11,000 3D videos and 1 million images across private and public clouds. Experiments showed the system achieved high accuracy in detecting copies while also being scalable.
Automated hierarchical classification of scanned documents using convolutiona...IJECEIAES
This research proposed automated hierarchical classification of scanned documents with characteristics content that have unstructured text and special patterns (specific and short strings) using convolutional neural network (CNN) and regular expression method (REM). The research data using digital correspondence documents with format PDF images from Pusat Data Teknologi dan Informasi (Technology and Information Data Center). The document hierarchy covers type of letter, type of manuscript letter, origin of letter and subject of letter. The research method consists of preprocessing, classification, and storage to database. Preprocessing covers extraction using Tesseract optical character recognition (OCR) and formation of word document vector with Word2Vec. Hierarchical classification uses CNN to classify 5 types of letters and regular expression to classify 4 types of manuscript letter, 15 origins of letter and 25 subjects of letter. The classified documents are stored in the Hive database in Hadoop big data architecture. The amount of data used is 5200 documents, consisting of 4000 for training, 1000 for testing and 200 for classification prediction documents. The trial result of 200 new documents is 188 documents correctly classified and 12 documents incorrectly classified. The accuracy of automated hierarchical classification is 94%. Next, the search of classified scanned documents based on content can be developed.
INFORMATION SECURITY THROUGH IMAGE WATERMARKING USING LEAST SIGNIFICANT BIT A...cscpconf
The rapid advancement of internet has made it easier to send the data/image accurate and
faster to the destination. Besides this, it is easier to modify and misuse the valuable information
through hacking at the same time. In order to transfer the data/image securely to the destination
without any modifications, there are many approaches like Cryptography, Watermarking and
Steganography. This paper presents the general overview of image watermarking and different
security issues. In this paper, Image Watermarking using Least Significant Bit (LSB) algorithm
has been used for embedding the message/logo into the image. This work has been implemented
through MATLAB
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online
Lsb hiding using random approach for image watermarkingeSAT Journals
Abstract A digital image watermarking is the process of embedding an image with a secondary parameter called watermark, without deterioration in the quality of image to provide copyright protection means to provide protection for intellectual property from illegal copying. In this paper the method of nested digital image watermarking is used that means a watermark inside another watermark embedded into the cover image that is the main image. Here the Randomized LSB hiding algorithm is used for embedding one image into another as it has lesser complexity and the approach is more robust to the variations in the type of image. The blowfish algorithm is used to encrypt the watermark image before embedding into the cover image. The concept of encryption of watermark image before get embedded into the main image is used here to increase the security of the watermark image. This is because the research work is mainly focus on to get the more secured watermark by improving and enhancing the embedding capacity. Key Words: Digital image Watermarking, Randomized LSB, Blowfish, Copyright Protection
This document discusses a randomized LSB hiding approach for nested digital image watermarking. It proposes encrypting one watermark image using Blowfish before embedding it into another watermark image using randomized LSB hiding. This nested watermark is then encrypted again using Blowfish before being embedded into the cover image for increased security. Randomized LSB hiding is used for embedding as it has lower complexity and is more robust than direct LSB hiding. The approach aims to improve security and embedding capacity for copyright protection of digital images.
IEEE 2014 DOTNET CLOUD COMPUTING PROJECTS Web image re ranking using query-sp...IEEEMEMTECHSTUDENTPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
The document summarizes recent advances in digital image watermarking techniques. It discusses how watermarking techniques can be classified based on the domain of embedding (spatial or transform), robustness to attacks (fragile or robust), and need for cover data during extraction (blind or non-blind). Specifically, it describes spatial domain techniques like least significant bit insertion and transform domain techniques using discrete Fourier transform, discrete cosine transform, and discrete wavelet transform. Transform techniques embed watermarks by modifying transform coefficients rather than pixel values.
This document describes a proposed cloud-based system for protecting multimedia content from unauthorized copying. The key components of the system include a crawler to download content from online sites, a novel signature method for creating fingerprints of 3D videos that captures depth signals, and a distributed matching engine that stores signatures and matches them against queries in a scalable way. The system was tested on over 11,000 3D videos and 1 million images across private and public clouds. Experiments showed the system achieved high accuracy in detecting copies while also being scalable.
Automated hierarchical classification of scanned documents using convolutiona...IJECEIAES
This research proposed automated hierarchical classification of scanned documents with characteristics content that have unstructured text and special patterns (specific and short strings) using convolutional neural network (CNN) and regular expression method (REM). The research data using digital correspondence documents with format PDF images from Pusat Data Teknologi dan Informasi (Technology and Information Data Center). The document hierarchy covers type of letter, type of manuscript letter, origin of letter and subject of letter. The research method consists of preprocessing, classification, and storage to database. Preprocessing covers extraction using Tesseract optical character recognition (OCR) and formation of word document vector with Word2Vec. Hierarchical classification uses CNN to classify 5 types of letters and regular expression to classify 4 types of manuscript letter, 15 origins of letter and 25 subjects of letter. The classified documents are stored in the Hive database in Hadoop big data architecture. The amount of data used is 5200 documents, consisting of 4000 for training, 1000 for testing and 200 for classification prediction documents. The trial result of 200 new documents is 188 documents correctly classified and 12 documents incorrectly classified. The accuracy of automated hierarchical classification is 94%. Next, the search of classified scanned documents based on content can be developed.
INFORMATION SECURITY THROUGH IMAGE WATERMARKING USING LEAST SIGNIFICANT BIT A...cscpconf
The rapid advancement of internet has made it easier to send the data/image accurate and
faster to the destination. Besides this, it is easier to modify and misuse the valuable information
through hacking at the same time. In order to transfer the data/image securely to the destination
without any modifications, there are many approaches like Cryptography, Watermarking and
Steganography. This paper presents the general overview of image watermarking and different
security issues. In this paper, Image Watermarking using Least Significant Bit (LSB) algorithm
has been used for embedding the message/logo into the image. This work has been implemented
through MATLAB
IRJET - A Genetic Approach for Reversible Database Watermarking using Fingerp...IRJET Journal
This document proposes a reversible database watermarking technique called Genetic Fingerprinting Algorithm using Histogram shifting (GFAHS). The technique encrypts the original database along with the owner's fingerprint and watermarks them together. This protects the original content from illegal ownership violations while allowing recovery of the original data. The technique uses a genetic algorithm to randomly partition and distribute the database among team members for collaborative work. When work is complete, updates are sent to team leaders and combined and forwarded to managers, who use biometric authentication to view the results. The aim is to provide a secure and robust system for group sharing of data on databases in the cloud.
Ensuring Distributed Accountability for Data Sharing Using Reversible Data Hi...IOSR Journals
Recently, more and more attention is paid to reversible data hiding (RDH) in encrypted images,
since it maintains the excellent property that the original cover can be lossless recovered after embedded data is
extracted while protecting the image content’s confidentiality. All previous methods embed data by reversibly
vacating room from the encrypted images, which may be subject to some errors on data extraction and/or image
restoration. In this paper, we propose a novel method by reserving room before encryption with a traditional
RDH algorithm, and thus it is easy for the data hider to reversibly embed data in the encrypted image. The
proposed method can achieve real reversibility, that is, data extraction and image recovery are free of any
error. A major feature of the centralized database services is that users’ data are usually processed remotely in
unknown machines that users do not own or operate. While enjoying the convenience brought by this new
emerging technology, users’ fears of losing control of their own data (particularly, financial and health data)
can become a significant barrier to the wide adoption of centralized database services. To address this problem,
in this paper, we propose a novel highly decentralized information accountability framework to keep track of the
actual usage of the user’s data in the cloud Over-lay Network. We leverage the LOG file create a dynamic and
traveling object, and to ensure that any access to users’ data will trigger authentication and automated logging
local to the LOGs. To strengthen user’s control, we also provide distributed auditing mechanisms. We provide
extensive experimental studies that demonstrate the efficiency and effectiveness of the proposed approaches.
Index Terms : Reversible data hiding, image encryption, privacy protection, data sharing.
We propose a new design for large-scale multimedia content protection systems.
The proposed system can be used to protect different multimedia content types, including 2-D videos, 3-D videos, images, audio clips, songs, and music clips.
The system can be deployed on private and/or public clouds. Our system has two novel components: (i) method to create signatures of 3-D videos, and (ii) distributed matching engine for multimedia objects.
bulk ieee projects in pondicherry,ieee projects in pondicherry,final year ieee projects in pondicherry
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
DECENTRALIZED ACCESS CONTROL OF DATA STORED IN CLOUD USING KEY POLICY ATTRIBU...Migrant Systems
This document proposes a decentralized access control method for data stored in the cloud using key policy attribute-based encryption (KP-ABE). It aims to allow fine-grained access control while maintaining data confidentiality and scaling efficiently. The method defines and implements access policies based on data attributes. It also allows the data owner to delegate access control tasks to cloud servers without revealing data contents. This is achieved using a combination of decentralized KP-ABE and a time-based file deletion scheme. The proposed approach is analyzed and shown to be highly secure and efficient.
Psdot 13 robust data leakage and email filtering systemZTech Proje
FINAL YEAR IEEE PROJECTS,
EMBEDDED SYSTEMS PROJECTS,
ENGINEERING PROJECTS,
MCA PROJECTS,
ROBOTICS PROJECTS,
ARM PIC BASED PROJECTS, MICRO CONTROLLER PROJECTS Z Technologies, Chennai
Data Leakage Detection and Security Using Cloud ComputingIJERA Editor
The data owner will store the data in the cloud. Every user must registered in the cloud. Cloud provider must
verify the authorized user. If someone try to access the account, data will get leaked. This leaked data will
present in an unauthorized place (e.g., on the internet or someone’s laptop). In this paper, we propose Division
and Replication of Data in the Cloud for Optimal Performance and Security (DROPS) that collectively
approaches the security and performance issues. In DROPS methodology, we have to select the file and then
store the particular file in the cloud account. In order to provide security we are going to implement DROPS
concepts. Now we divide the file into various fragments based on the threshold value. Each and every fragments
are stored in the node using T-Coloring. After the placement of fragments in node, it is necessary to replicate
each fragments for one time in cloud.
The proposed system aims to address users' privacy concerns about data sharing in the cloud by developing a decentralized information accountability framework. The framework leverages JAR capabilities to create dynamic traveling objects that enclose user data, policies, and an automated logging mechanism. Any access to the enclosed data will trigger authentication and logging. Distributed auditing mechanisms are also provided to strengthen user control. The framework was experimentally evaluated and shown to efficiently and effectively track data usage in the cloud in a decentralized manner while minimizing overhead.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A Generalized Image Authentication Based On Statistical Moments of Color Hist...idescitation
Designing low cost and high speed authentication
solution for digital images is always an attractive area of
research in image processing. In past few years because of
widespread use of internet and network technology, concept
of information distribution has been become habit rather than
exception in daily life. In same aspects challenges involved
with distribution of authenticate information has been
increased manifolds. In this paper a generalize image
authentication method has proposed by hybridization of color
histogram and associated first four statistical moments to
achieve the objectives of low cost and high speed. Proposed
method can apply for both gray and color images having any
size and any format. Solution generates a very small
authentication code with an ease means which is use to analyze
the characteristics of received image from tampering
perspective.
Cloud Computing Environment using Secured Access Control TechniqueIRJET Journal
This document proposes a new technique called Storage Correctness and Small-grained Access Provision (SCSAP) for secure cloud computing environments. SCSAP aims to improve on existing access control techniques by providing more fine-grained user access and ensuring correctness of outsourced cloud data storage through a token granting system. The technique constructs hierarchical user access formations and includes algorithmic phases for small-grained data access and efficient storage. If implemented, SCSAP could provide stronger security, access control and data integrity than prior cloud storage solutions.
Optimized WES-System with Image Bit Embedding for Enhancing the Security of H...IRJET Journal
This document proposes an optimized security method for transmitting images over networks that combines watermarking, steganography, and embedding another image within the host image. The method works by first watermarking the host image in both its image and text form to obscure it. Then another image is embedded within the watermarked host image as a carrier, providing another layer of security. This combined output image is then transmitted to the receiver, who can extract both the original host image and watermark text using extraction techniques. The goal of this optimized approach is to provide stronger security and resistance to unauthorized access during transmission compared to prior individual techniques.
This document proposes a self-assured deduplication system for authorized deduplication in a hybrid cloud. The system uses convergent encryption to encrypt data before uploading it to the public cloud while allowing for deduplication. A private cloud server manages the private keys and generates file tokens to allow authorized duplicate checking on the public cloud. The proposed system was implemented as a prototype and experiments show it incurs minimal overhead compared to normal operations like encryption and uploading.
IRJET - Identifying Information Relocate with Reliable Estimation and Sec...IRJET Journal
This document summarizes a research paper that proposes a method for ensuring data integrity and privacy when data is stored on cloud computing systems. The method uses blockchain techniques and distributed verification to provide redundancy and guarantee data reliability. It allows both data owners and public verifiers to check data integrity without downloading the entire dataset. The technique utilizes homomorphic tokens and ring signatures to enable auditing while preventing privacy leaks about user identities or data contents. Prior works on remote data integrity lacked either public auditing or support for dynamic data operations, but the proposed method achieves both.
This document discusses enhancing security through token generation in a distributed environment. It proposes a new token generation scheme to encrypt user data with specified key parameters, making resources more robust. The token generation scheme would add security for both authentication and authorization. Existing algorithms focus on encrypting data on the user side, which incurs high computational and communication costs. The document suggests a token generation algorithm for distributed data files that provides secure and dependable server storage while maintaining low overhead. It analyzes related work on token-based authentication and security techniques to provide context.
The protection of multimedia data is becoming very
important. The protection of this multimedia data can be done
with encryption or data hiding algorithms. To decrease
transmissions time the data transmission necessary.
Recently, more and more attention is paid to reversible data
hiding (RDH) in encrypted image. It maintains original area
could be perfectly restored after extraction of the hidden
message. In previous method embed data by reversibly vacating
area from the encrypted images, which may be subject to some
errors on data extraction and/or image restoration. A novel
method by reserving area before encryption with a traditional
RDH algorithm, and thus it is easy for the data hider to
reversibly embed data in the encrypted image. The proposed
method can achieve real reversibility, that is data extraction and
image recovery are free of any error. The hidden data can be
retrieved as and when required. The methods that are used in
reversible data hiding techniques like Lossless embedding and
encryption.
This deals with the image steganography as well as with the
different security issues, general overview of cryptography
approaches and about the different steganography
algorithms like Least Significant Bit (LSB) algorithm ,
JSteg, F5 algorithms. It also compares those algorithms in
means of speed, accuracy and security.
documentation for identity based secure distrbuted data storage schemesSahithi Naraparaju
This document proposes two identity-based secure distributed data storage schemes. The first scheme provides confidentiality against chosen plaintext attacks, while the second achieves confidentiality against chosen ciphertext attacks. The schemes allow a file owner to independently set access permissions for individual files without help from a private key generator. They also protect against collusion attacks between receivers and proxy servers. To the best of the author's knowledge, these are the first identity-based distributed storage schemes that allow file-level access control and protect against collusion in the standard model.
Shared Authority Based Privacy-preserving Authentication Protocol in Cloud Co...Migrant Systems
The document proposes a shared authority based privacy-preserving authentication protocol (SAPA) for cloud computing. SAPA addresses the privacy issue that arises when a user challenges a cloud server to request access to another user's data, as the request itself could reveal private information. SAPA uses anonymous access request matching and attribute-based access control to determine if two users' access requests are mutually compatible without revealing either user's private access desires. It also employs proxy re-encryption so the cloud server can provide temporary shared access between authorized users. The protocol aims to simultaneously achieve data access control, authority sharing between compatible users, and protection of users' privacy during the access request process.
A New Watermarking Approach Based on Combination of Reversible Watermarking a...CSCJournals
Image watermarking can be defined as a technique that allows insertion of imperceptible and indelible digital data into an image. In addition to its initial application which is the copyright, watermarking can be used in other fields, particularly in the medical field in order to contribute to secure images shared on the network for telemedicine applications. In this report we study some watermarking methods and the comparison result of their combination, the first one is based on the CDMA (Code Division Multiple Access) in DWT and spatial domain and its aim is to verify the image authenticity whereas the second one is the reversible watermarking (the least significant bits LSB and cryptography tools) and the reversible carte mapping RCM its objective is to check the integrity of the image and to keep the Confidentiality of the patient data. A new scheme of watermarking is the combination of the reversible watermarking method based on LSB and cryptography tools and the method of CDMA in spatial and DWT domain to verify the three security properties Integrity, Authenticity and confidentiality of medical data and patient information .In the end ,we made a comparison between these methods within the parameters of quality of medical images. Initially, an in-depth study on the characteristics of medical images would contribute to improve these methods to mitigate their limits and to optimize the results. Tests were done on IRM kind of medical images and the quality measurements have been done on the watermarked image to verify that this technique does not lead to a wrong diagnostic. The robustness of the watermarked images against attacks has been verified on the parameters of PSNR, SNR, MSE and MAE which the experimental result demonstrated that the proposed algorithm is good and robust in DWT than in spatial domain.
The document provides an overview of digital watermarking. It defines watermarking as imperceptibly altering a work to embed a message about the work. The document outlines the history of watermarking and discusses its applications, including owner identification, proof of ownership, broadcast monitoring, transaction tracking, and content authentication. It also compares watermarking to other techniques like cryptography and discusses the importance of digital watermarking for copyright protection in the digital age.
Data Security In Relational Database Management SystemCSCJournals
Proving ownerships rights on outsourced relational database is a crucial issue in today\'s internet based application environments and in many content distribution applications. Here mechanism is proposed for proof of ownership based on the secure embedding of a robust imperceptible watermark in relational data. Watermarking of relational databases as a constrained optimization problem and discus efficient techniques to solve the optimization problem and to handle the constraints. This watermarking technique is resilient to watermark synchronization errors because it uses a partioning approach that does not require marker tuple. This approach overcomes a major weakness in previously proposed watermarking techniques. Watermark decoding is based on a threshold-based technique characterized by an optimal threshold that minimizes the probability of decoding errors. An implemented a proof of concept implementation of our watermarking technique and showed by experimental results that our technique is resilient to tuple deletion, alteration and insertion attacks.
IRJET - A Genetic Approach for Reversible Database Watermarking using Fingerp...IRJET Journal
This document proposes a reversible database watermarking technique called Genetic Fingerprinting Algorithm using Histogram shifting (GFAHS). The technique encrypts the original database along with the owner's fingerprint and watermarks them together. This protects the original content from illegal ownership violations while allowing recovery of the original data. The technique uses a genetic algorithm to randomly partition and distribute the database among team members for collaborative work. When work is complete, updates are sent to team leaders and combined and forwarded to managers, who use biometric authentication to view the results. The aim is to provide a secure and robust system for group sharing of data on databases in the cloud.
Ensuring Distributed Accountability for Data Sharing Using Reversible Data Hi...IOSR Journals
Recently, more and more attention is paid to reversible data hiding (RDH) in encrypted images,
since it maintains the excellent property that the original cover can be lossless recovered after embedded data is
extracted while protecting the image content’s confidentiality. All previous methods embed data by reversibly
vacating room from the encrypted images, which may be subject to some errors on data extraction and/or image
restoration. In this paper, we propose a novel method by reserving room before encryption with a traditional
RDH algorithm, and thus it is easy for the data hider to reversibly embed data in the encrypted image. The
proposed method can achieve real reversibility, that is, data extraction and image recovery are free of any
error. A major feature of the centralized database services is that users’ data are usually processed remotely in
unknown machines that users do not own or operate. While enjoying the convenience brought by this new
emerging technology, users’ fears of losing control of their own data (particularly, financial and health data)
can become a significant barrier to the wide adoption of centralized database services. To address this problem,
in this paper, we propose a novel highly decentralized information accountability framework to keep track of the
actual usage of the user’s data in the cloud Over-lay Network. We leverage the LOG file create a dynamic and
traveling object, and to ensure that any access to users’ data will trigger authentication and automated logging
local to the LOGs. To strengthen user’s control, we also provide distributed auditing mechanisms. We provide
extensive experimental studies that demonstrate the efficiency and effectiveness of the proposed approaches.
Index Terms : Reversible data hiding, image encryption, privacy protection, data sharing.
We propose a new design for large-scale multimedia content protection systems.
The proposed system can be used to protect different multimedia content types, including 2-D videos, 3-D videos, images, audio clips, songs, and music clips.
The system can be deployed on private and/or public clouds. Our system has two novel components: (i) method to create signatures of 3-D videos, and (ii) distributed matching engine for multimedia objects.
bulk ieee projects in pondicherry,ieee projects in pondicherry,final year ieee projects in pondicherry
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
DECENTRALIZED ACCESS CONTROL OF DATA STORED IN CLOUD USING KEY POLICY ATTRIBU...Migrant Systems
This document proposes a decentralized access control method for data stored in the cloud using key policy attribute-based encryption (KP-ABE). It aims to allow fine-grained access control while maintaining data confidentiality and scaling efficiently. The method defines and implements access policies based on data attributes. It also allows the data owner to delegate access control tasks to cloud servers without revealing data contents. This is achieved using a combination of decentralized KP-ABE and a time-based file deletion scheme. The proposed approach is analyzed and shown to be highly secure and efficient.
Psdot 13 robust data leakage and email filtering systemZTech Proje
FINAL YEAR IEEE PROJECTS,
EMBEDDED SYSTEMS PROJECTS,
ENGINEERING PROJECTS,
MCA PROJECTS,
ROBOTICS PROJECTS,
ARM PIC BASED PROJECTS, MICRO CONTROLLER PROJECTS Z Technologies, Chennai
Data Leakage Detection and Security Using Cloud ComputingIJERA Editor
The data owner will store the data in the cloud. Every user must registered in the cloud. Cloud provider must
verify the authorized user. If someone try to access the account, data will get leaked. This leaked data will
present in an unauthorized place (e.g., on the internet or someone’s laptop). In this paper, we propose Division
and Replication of Data in the Cloud for Optimal Performance and Security (DROPS) that collectively
approaches the security and performance issues. In DROPS methodology, we have to select the file and then
store the particular file in the cloud account. In order to provide security we are going to implement DROPS
concepts. Now we divide the file into various fragments based on the threshold value. Each and every fragments
are stored in the node using T-Coloring. After the placement of fragments in node, it is necessary to replicate
each fragments for one time in cloud.
The proposed system aims to address users' privacy concerns about data sharing in the cloud by developing a decentralized information accountability framework. The framework leverages JAR capabilities to create dynamic traveling objects that enclose user data, policies, and an automated logging mechanism. Any access to the enclosed data will trigger authentication and logging. Distributed auditing mechanisms are also provided to strengthen user control. The framework was experimentally evaluated and shown to efficiently and effectively track data usage in the cloud in a decentralized manner while minimizing overhead.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A Generalized Image Authentication Based On Statistical Moments of Color Hist...idescitation
Designing low cost and high speed authentication
solution for digital images is always an attractive area of
research in image processing. In past few years because of
widespread use of internet and network technology, concept
of information distribution has been become habit rather than
exception in daily life. In same aspects challenges involved
with distribution of authenticate information has been
increased manifolds. In this paper a generalize image
authentication method has proposed by hybridization of color
histogram and associated first four statistical moments to
achieve the objectives of low cost and high speed. Proposed
method can apply for both gray and color images having any
size and any format. Solution generates a very small
authentication code with an ease means which is use to analyze
the characteristics of received image from tampering
perspective.
Cloud Computing Environment using Secured Access Control TechniqueIRJET Journal
This document proposes a new technique called Storage Correctness and Small-grained Access Provision (SCSAP) for secure cloud computing environments. SCSAP aims to improve on existing access control techniques by providing more fine-grained user access and ensuring correctness of outsourced cloud data storage through a token granting system. The technique constructs hierarchical user access formations and includes algorithmic phases for small-grained data access and efficient storage. If implemented, SCSAP could provide stronger security, access control and data integrity than prior cloud storage solutions.
Optimized WES-System with Image Bit Embedding for Enhancing the Security of H...IRJET Journal
This document proposes an optimized security method for transmitting images over networks that combines watermarking, steganography, and embedding another image within the host image. The method works by first watermarking the host image in both its image and text form to obscure it. Then another image is embedded within the watermarked host image as a carrier, providing another layer of security. This combined output image is then transmitted to the receiver, who can extract both the original host image and watermark text using extraction techniques. The goal of this optimized approach is to provide stronger security and resistance to unauthorized access during transmission compared to prior individual techniques.
This document proposes a self-assured deduplication system for authorized deduplication in a hybrid cloud. The system uses convergent encryption to encrypt data before uploading it to the public cloud while allowing for deduplication. A private cloud server manages the private keys and generates file tokens to allow authorized duplicate checking on the public cloud. The proposed system was implemented as a prototype and experiments show it incurs minimal overhead compared to normal operations like encryption and uploading.
IRJET - Identifying Information Relocate with Reliable Estimation and Sec...IRJET Journal
This document summarizes a research paper that proposes a method for ensuring data integrity and privacy when data is stored on cloud computing systems. The method uses blockchain techniques and distributed verification to provide redundancy and guarantee data reliability. It allows both data owners and public verifiers to check data integrity without downloading the entire dataset. The technique utilizes homomorphic tokens and ring signatures to enable auditing while preventing privacy leaks about user identities or data contents. Prior works on remote data integrity lacked either public auditing or support for dynamic data operations, but the proposed method achieves both.
This document discusses enhancing security through token generation in a distributed environment. It proposes a new token generation scheme to encrypt user data with specified key parameters, making resources more robust. The token generation scheme would add security for both authentication and authorization. Existing algorithms focus on encrypting data on the user side, which incurs high computational and communication costs. The document suggests a token generation algorithm for distributed data files that provides secure and dependable server storage while maintaining low overhead. It analyzes related work on token-based authentication and security techniques to provide context.
The protection of multimedia data is becoming very
important. The protection of this multimedia data can be done
with encryption or data hiding algorithms. To decrease
transmissions time the data transmission necessary.
Recently, more and more attention is paid to reversible data
hiding (RDH) in encrypted image. It maintains original area
could be perfectly restored after extraction of the hidden
message. In previous method embed data by reversibly vacating
area from the encrypted images, which may be subject to some
errors on data extraction and/or image restoration. A novel
method by reserving area before encryption with a traditional
RDH algorithm, and thus it is easy for the data hider to
reversibly embed data in the encrypted image. The proposed
method can achieve real reversibility, that is data extraction and
image recovery are free of any error. The hidden data can be
retrieved as and when required. The methods that are used in
reversible data hiding techniques like Lossless embedding and
encryption.
This deals with the image steganography as well as with the
different security issues, general overview of cryptography
approaches and about the different steganography
algorithms like Least Significant Bit (LSB) algorithm ,
JSteg, F5 algorithms. It also compares those algorithms in
means of speed, accuracy and security.
documentation for identity based secure distrbuted data storage schemesSahithi Naraparaju
This document proposes two identity-based secure distributed data storage schemes. The first scheme provides confidentiality against chosen plaintext attacks, while the second achieves confidentiality against chosen ciphertext attacks. The schemes allow a file owner to independently set access permissions for individual files without help from a private key generator. They also protect against collusion attacks between receivers and proxy servers. To the best of the author's knowledge, these are the first identity-based distributed storage schemes that allow file-level access control and protect against collusion in the standard model.
Shared Authority Based Privacy-preserving Authentication Protocol in Cloud Co...Migrant Systems
The document proposes a shared authority based privacy-preserving authentication protocol (SAPA) for cloud computing. SAPA addresses the privacy issue that arises when a user challenges a cloud server to request access to another user's data, as the request itself could reveal private information. SAPA uses anonymous access request matching and attribute-based access control to determine if two users' access requests are mutually compatible without revealing either user's private access desires. It also employs proxy re-encryption so the cloud server can provide temporary shared access between authorized users. The protocol aims to simultaneously achieve data access control, authority sharing between compatible users, and protection of users' privacy during the access request process.
A New Watermarking Approach Based on Combination of Reversible Watermarking a...CSCJournals
Image watermarking can be defined as a technique that allows insertion of imperceptible and indelible digital data into an image. In addition to its initial application which is the copyright, watermarking can be used in other fields, particularly in the medical field in order to contribute to secure images shared on the network for telemedicine applications. In this report we study some watermarking methods and the comparison result of their combination, the first one is based on the CDMA (Code Division Multiple Access) in DWT and spatial domain and its aim is to verify the image authenticity whereas the second one is the reversible watermarking (the least significant bits LSB and cryptography tools) and the reversible carte mapping RCM its objective is to check the integrity of the image and to keep the Confidentiality of the patient data. A new scheme of watermarking is the combination of the reversible watermarking method based on LSB and cryptography tools and the method of CDMA in spatial and DWT domain to verify the three security properties Integrity, Authenticity and confidentiality of medical data and patient information .In the end ,we made a comparison between these methods within the parameters of quality of medical images. Initially, an in-depth study on the characteristics of medical images would contribute to improve these methods to mitigate their limits and to optimize the results. Tests were done on IRM kind of medical images and the quality measurements have been done on the watermarked image to verify that this technique does not lead to a wrong diagnostic. The robustness of the watermarked images against attacks has been verified on the parameters of PSNR, SNR, MSE and MAE which the experimental result demonstrated that the proposed algorithm is good and robust in DWT than in spatial domain.
The document provides an overview of digital watermarking. It defines watermarking as imperceptibly altering a work to embed a message about the work. The document outlines the history of watermarking and discusses its applications, including owner identification, proof of ownership, broadcast monitoring, transaction tracking, and content authentication. It also compares watermarking to other techniques like cryptography and discusses the importance of digital watermarking for copyright protection in the digital age.
Data Security In Relational Database Management SystemCSCJournals
Proving ownerships rights on outsourced relational database is a crucial issue in today\'s internet based application environments and in many content distribution applications. Here mechanism is proposed for proof of ownership based on the secure embedding of a robust imperceptible watermark in relational data. Watermarking of relational databases as a constrained optimization problem and discus efficient techniques to solve the optimization problem and to handle the constraints. This watermarking technique is resilient to watermark synchronization errors because it uses a partioning approach that does not require marker tuple. This approach overcomes a major weakness in previously proposed watermarking techniques. Watermark decoding is based on a threshold-based technique characterized by an optimal threshold that minimizes the probability of decoding errors. An implemented a proof of concept implementation of our watermarking technique and showed by experimental results that our technique is resilient to tuple deletion, alteration and insertion attacks.
ROBUST LOSSLESS WATERMARKING OF RELATIONAL DATABASES USING MULTIMEDIA DATA_An...anjuvipin
The document discusses robust lossless watermarking of relational databases using multimedia data. It provides background on existing database watermarking techniques and their limitations. The proposed methodology embeds watermarks in a relational database by modulating color image planes and histograms in a way that is robust to attacks while inducing minimal distortion. It creates hash functions to make watermarks independent of the database contents. Experimental results show the technique achieves 100% detection even when the database size increases 100% and is robust against subset insertion, deletion and alteration attacks, with low mean squared error and high peak signal to noise ratio. The watermarking can be extended to video frames.
This document provides an overview of digital watermarking. It defines watermarking as applying information hiding techniques to hide watermarks in digital media like images. Watermarking can be either perceptible or imperceptible depending on the application. The document discusses various applications of watermarking like copyright protection, tamper proofing, and quality assessment. It also covers watermarking techniques, categories based on extraction and robustness, and considerations for designing watermarking algorithms like capacity, security, robustness, and imperceptibility.
Digital Watermarking describes methods and technologies that hide information, for example a number or text, in digital media, such as images, video. The embedding takes place by manipulating the content of the digital data, which means the information is not embedded in the frame around the data. The hiding process has to be such that the modifications of the media are imperceptible. For images this means that the modifications of the pixel values have to be invisible.
A digital watermark is a message which is embedded into digital content (video, images or text) that can be detected or extracted later. Moreover, in image the actual bits representing the watermark must be scattered throughout the file in such a way that they cannot be identified and manipulated. Watermarking is the insertion of imperceptible and inseparable information into the host data for data security & integrity. They are characterizing patterns, of varying visibility, added to the presentation media as a guarantee of authenticity, quality, ownership, and source. However, in digital watermarking, the message is supposed not to visible (or at least not interfering with the user experience of the content), but (only) electronic devices can retrieve the embedded message to identify the code. Another form of digital watermarking is known as steganography, in which a message is hidden in the content without typical citizens or the public authorities noticing its presence. Only a limited number of recipients can retrieve and decode the hidden message. Unlike a traditional watermark on paper, which is generally visible to the eye, digital watermarks can be made invisible or inaudible. They can, however, be read by a computer with the proper decoding software.
This document summarizes a student project on reversible data hiding techniques. The project compares different reversible watermarking methods and proposes a new technique that embeds a secret bitstream into a color image using bisection and square root interpolation. Experimental results showed the embedded and extracted bitstreams had a correlation of 1, indicating no data loss. Future work could improve the algorithm security by using multiple color planes and transformations for watermarking.
The document proposes a new video watermarking algorithm using the dual-tree complex wavelet transform (DTCWT). The DTCWT offers advantages like shift invariance and directional selectivity. The algorithm embeds a watermark by adding its coefficients to high frequency DTCWT coefficients of video frames. Masks are used to hide the watermark perceptually. Experimental results show the proposed method is robust to geometric distortions, lossy compression, and a joint attack, outperforming comparable DWT-based methods. It is suitable for playback control due to its robustness and simple implementation.
This document discusses different techniques for digital image watermarking, including in the spatial and frequency domains. It provides an overview of watermarking concepts and applications. It then describes two watermarking algorithms - one that embeds watermarks in the spatial domain by modifying pixel intensities in selected image blocks, and another that embeds watermarks in the wavelet domain by modifying selected wavelet coefficients. Both algorithms are described step-by-step and include watermark insertion and extraction procedures. Results are provided showing the performance of the algorithms under different attacks in terms of normalized cross-correlation between the original and extracted watermarks.
Digital image watermarking is a technique to hide information (the watermark) within an image. It can be used for identification, authentication, and copyright protection. There are different domains to embed watermarks, including the spatial, wavelet, and frequency domains. The watermark is imperceptible, robust, inseparable from the image, and provides security. Watermarks can be extracted from the watermarked image after embedding.
Lsb Based Digital Image Watermarking For Gray Scale ImageIOSR Journals
The document describes a technique for watermarking grayscale images using the least significant bit (LSB) method. It begins with an abstract that introduces digital watermarking and LSB watermarking. It then provides more details on the LSB algorithm and how it embeds a watermark by replacing the LSB of selected image pixels. The paper tests the technique on various images, embedding the watermark in different bit positions. It calculates the mean squared error and peak signal-to-noise ratio for the watermarked images. Finally, it applies different noise attacks to the watermarked images and measures the effect on quality.
A Review of BSS Based Digital Image Watermarking and Extraction MethodsIOSR Journals
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A Review of BSS Based Digital Image Watermarking and Extraction MethodsIOSR Journals
Abstract :The field of Signal Processing has witnessed the strong emergence of a new technique, the Blind Signal Processing (BSP) which is based on sound theoretical foundation. An offshoot of the BSP is known as Blind Source Separation (BSS). This digital signal processing techniques have a wide and varied potential applications. The term blind is indicative of the fact that both the source signal and the mixing procedures are unknown. One of the more interesting applications of BSS is in field of image data security/authentication where digital watermarking is proposed. Watermarking is a promising technique to help protect data security and intellectual property rights. The plethora digital image watermarking methods are surveyed and discussed here with their features and limitations. Thus literature survey is presented in two major categories-Digital image watermarking methods and BSS based techniques in digital image watermarking and extraction. Keywords – BSP, BSS, Mixing Coefficient, Digital Image Watermarking, Watermark Extraction.
IRJET- An Efficient and Robust Approach for Relational Databases WatermarkingIRJET Journal
The document describes a proposed efficient and robust approach for watermarking relational databases. The key aspects of the proposed approach are:
1) It partitions the database into clusters and embeds the watermark bits into multiple attributes across clusters, making it difficult for attackers to remove the watermark.
2) During watermark encoding, bits are embedded into selected attributes and bit locations of tuples in each cluster based on secret key and tuple values.
3) Majority voting is used during decoding to correctly decode watermark bits, even if some tuples are corrupted by attackers.
4) The approach aims to embed watermarks with minimal data distortion and preserve data usability after watermarking.
ANALYSIS OF IMAGE WATERMARKING USING LEAST SIGNIFICANT BIT ALGORITHMijistjournal
The rapid advancement of internet has made it easier to send the data/image accurate and faster to the destination. But thisadvantage is also accompanied with the disadvantage of modifying and misusing the valuable information through intercepting or hacking.So In order to transfer the data/image to the intended user at destination without anyalterations or modifications, there are many approaches like Cryptography, Watermarking and Steganography. This paper presents the general overview of image watermarking and different security issues. In this paper, Image Watermarking using Least Significant Bit (LSB) algorithm has been used for embedding the message/logo into the image. This work has been implemented through MATLAB.
ANALYSIS OF IMAGE WATERMARKING USING LEAST SIGNIFICANT BIT ALGORITHMijistjournal
The rapid advancement of internet has made it easier to send the data/image accurate and faster to the destination. But thisadvantage is also accompanied with the disadvantage of modifying and misusing the valuable information through intercepting or hacking.So In order to transfer the data/image to the intended user at destination without anyalterations or modifications, there are many approaches like Cryptography, Watermarking and Steganography. This paper presents the general overview of image watermarking and different security issues. In this paper, Image Watermarking using Least Significant Bit (LSB) algorithm has been used for embedding the message/logo into the image. This work has been implemented through MATLAB.
IRJET-Security Based Data Transfer and Privacy Storage through Watermark Dete...IRJET Journal
Gowtham.T ,Pradeep Kumar.G " Security Based Data Transfer and Privacy Storage through Watermark Detection ", International Research Journal of Engineering and Technology (IRJET), Volume2,issue-01 April 2015.e-ISSN:2395-0056, p-ISSN:2395-0072. www.irjet.net .published by Fast Track Publications
Abstract
Digital watermarking has been proposed as a technology to ensure copyright protection by embedding an imperceptible, yet detectable signal in visual multimedia content such as images or video. In every field key aspect is the security Privacy is a critical issue when the data owners outsource data storage or processing to a third party computing service. Several attempts has been made for increasing the security related works and avoidance of data loss. Existing system had attain its solution up to its level where it can be further able to attain the parameter refinement. In this paper improvising factor been made on the successive compressive sensing reconstruction part and Peak Signal-to-Noise Ratio (PSNR).Another consideration factor is to increase (CS) rate through de-emphasize the effect of predictive variables that become uncorrelated with the measurement data which eliminates the need of (CS) reconstruction.
This document discusses a new approach to providing secure data transmission that combines digital watermarking and image compression techniques. Digital watermarking involves embedding hidden information in multimedia content like images, audio or video. The proposed approach uses discrete cosine transform (DCT) based watermarking combined with an improved adaptive Huffman encoding image compression algorithm. This combined technique aims to enhance security for data transmission while reducing storage space requirements compared to other compression methods.
This document discusses a proposed technique for secure data transmission that combines digital image watermarking and image compression. It begins with background information on digital watermarking, including its classifications, requirements, general system, and techniques such as spatial domain and frequency domain methods. It then provides an overview of image compression, including its benefits, techniques such as lossless and lossy compression, and common compression methods. The proposed technique embeds a watermark into an image using discrete cosine transform (DCT) based watermarking in the frequency domain. It then applies lossy image compression to the watermarked image using an improved adaptive Huffman coding algorithm. The goal is to achieve higher security for data transmission by combining these two techniques compared to
This document summarizes a research paper that proposes a fragile watermarking technique for image authentication using a hierarchical mechanism. The technique embeds watermark data at both the pixel and block levels of an image. At the receiver end, tampered blocks can first be identified using block-level watermarks. Then pixel-level watermarks in untampered blocks are used to precisely locate any tampered pixels. The technique aims to accurately locate tampered regions even if a large area is modified, and also allows perfect restoration of the original watermarked image.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Digital watermarking knowledge is a leading edge research field and it mainly focuses on the
intellectual property rights, hides data and embedded inside an image to show authenticity or proof
of ownership, discovery and authentication of the digital media to protect the important documents.
Digital watermarking can help to verify ownership, to recognize a misappropriate person and find the
marked documents. One of the significant technological actions of the last two decades was the
attack of digital media in a complete range of everyday life aspects.
Digital data can be stored efficiently with a very high quality and it can be manipulated very
easily using computers. In addition digital data can be transmitted in a fast and inexpensive way
through data communication networks without losing quality. According to the necessary study of
digital image watermarking, the digital watermarking model consists of two modules, which are
watermark embedding module and watermark extraction and detection module.
A Survey on Features Combination for Image WatermarkingEditor IJMTER
As the internet users are increasing day by day it is easy to transfer digital data. By this new
problem of data piracy is increasing. For this different methods of watermarking are developed for
protecting the digital data like video, audio, image, etc. Out of these many researcher are working on
image watermarking field from last few decades. This paper focus on the image watermarking features
combination with various techniques which are broadly categorized into spatial and frequency domain.
Many features are studied with their different requirement and functionality. It has been observed that
most of the researcher combines many features for achieving the prior goal of the watermark that is to
embed watermark and extract from the carrier image in presence of different attack.
Report on Digital Watermarking Technology vijay rastogi
Digital watermarking is the process of embedding information into digital multimedia content such that the information (which we call the watermark) can later be extracted or detected for a variety of purposes including copy prevention and control.
This paper presents a spatial domain digital image watermarking technique. The technique embeds a binary watermark image into the cover image by inserting watermark pixels into homogeneous blocks with low variance. The watermark is first dispersed using a chaotic system before insertion. A secret multilevel image is used to extract the watermark. Experimental results show the watermark is resilient against various attacks like mean filtering, Gaussian filtering, median filtering, image rescaling, and JPEG compression. The watermark can be extracted with high correlation even after these attacks.
A Review on Robust Digital Watermarking based on different Methods and its Ap...IJSRD
Digital Watermarking is the process of embedding data called watermark or signature or label or tag into a multimedia object (image or audio or video) so that the watermark can be extracted for ownership verification or authentication. A visible watermark is a secondary translucent image overlaid into the primary image and appears visible to a viewer on a careful inspection. The invisible watermark is embedded in such a way that the modification made to the pixel value is perceptually not noticed and it can be recovered only with an appropriate decoding mechanism. Digital watermarking is used to hide the information inside a signal, which cannot be easily extracted by the third party. Its widely used application is copyright protection of digital information. It is different from the encryption in the sense that it allows the user to access, view and interpret the signal but protect the ownership of the content. One of the current research areas is to protect digital watermark inside the information so that ownership of the information cannot be claimed by third party.
STAGE STAFFING SCHEME FOR COPYRIGHT PROTECTION IN MULTIMEDIAIJNSA Journal
Copyright protection has become a need in today’s world. To achieve a secure copyright protection we embedded some information in images and videos and that image or video is called copyright protected. The embedded information can’t be detected by human eye but some attacks and operations can tamper that information to breach protection. So in order to find a secure technique of copyright protection, we have analyzed image processing techniques i.e. Spatial Domain (Least Significant Bit (LSB)), Transform Domain (Discrete Cosine Transform (DCT)), Discrete Wavelet Transform (DWT) and there are numerous algorithm for watermarking using them. After having a good understanding of the same we have proposed a novel algorithm named as Stage Staffing Algorithm that generates results with high effectiveness, additionally we can use self extracted-watermark technique to increase the security and automate the process of watermark image. The proposed algorithm provides protection in three stages. We have implemented the algorithm and results of the simulations are shown. The various factors affecting spatial domain watermarking are also discussed.
A Brief Survey on Robust Video Watermarking Techniquestheijes
This document provides a survey of robust video watermarking techniques. It begins with an abstract discussing digital watermarking and its role in copyright protection as the growth of multimedia on the internet has led to more copyright issues. The document then reviews various video watermarking methods and factors like robustness, security, and perceptual fidelity. It discusses approaches like spatial domain and transform domain watermarking techniques that use discrete cosine transform, fast Fourier transform, and discrete wavelet transform. The document also provides a table comparing different video watermarking methods from past literature and concludes that watermarking combined with other cryptographic techniques can provide effective copyright protection for video.
What is watermarking?
The watermark describes information that can be used for proofing of ownership or tamper proofing.
Digital Watermarking describes methods and technologies that hide information, for example, a number or text, in digital media, such as images, video.
Why Watermarking
Real-world datasets can tolerate a small amount of error without degrading their usability
Effective means for proofing of ownership
Effective means for tamper proofing
Types of watermarking:
Visible watermarking
Invisible Watermarking
Application:
Source Tracking.
Broadcast Monitoring.
Content protection for audio and video content.
Forensics and piracy deterrence.
Communication of ownership and copyright.
Document and image security.
Copy prevention or control
The requirement of Watermarking:
Imperceptibility
Robustness
Security
Complexity
Verification
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Image Based Relational Database Watermarking: A Survey
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 3, Ver. 1 (May – Jun. 2015), PP 54-65
www.iosrjournals.org
DOI: 10.9790/0661-17315465 www.iosrjournals.org 54 | Page
Image Based Relational Database Watermarking: A Survey
Sapna Prajapati1,
Namita Tiwari2
(1
Department Of Computer Science Engineering NIT-Bhopal, India)
(2
Asst. Professor, Department Of Computer Science Engineering NIT-Bhopal, India)
Abstract: In past few years relational databases watermarking has emerged a great topic for research because
of increase in use of relational databases. The basic need for relational database watermarking is to prevent
data from illegal access by providing copyright protection, tamper detection and maintaining integrity. To serve
this purpose many database watermarking techniques have been addressed with different algorithm and cover
type. With the use of watermarking unauthorized duplication and distribution can be detected. The
watermarking scheme should be able to meet some important challenges: 1). The scheme should be capable
enough to bear attacks so that the watermark doesn’t get corrupted 2). The technique should be data preserving
i.e. the error introduced into database as watermark information should not affect the value of data. The basic
purpose of this paper is to review and analyse some existing image based database watermarking techniques.
Keywords: Relational database, database watermarking, copyright protection.
I. Introduction
The rapid growth of internet access and exchanging digital data online has made duplication and
distribution of the data easier than ever before. To overcome the challenges of forgery, false copyright claim,
illegal redistribution of data, etc. watermarking has emerged as great technology. A watermark is information
that is embedded into original data for ownership proof, copyright detection, traitor tracing etc. Since use of
database has increased enormously, database watermarking has gain much of the attention in last few years. Just
like encryption, typical watermarking will modify the ordinal data and will cause permanent distortion to the
original ones and this is an issue if integrity in the database is utmost important requirement. Basic idea behind
database watermarking is to change some of attributes value to another value, to such an extent so that the
distortion is minimal and tolerable. [2]
The watermark approach basically involves two phases: firstly, inserting the watermark into the database,
in which a private key K (known to the owner only) is used to embed the watermark information W into the
original database. This watermarked database is then made publicly available for use. Secondly, verifying the
watermark from the suspected database. The suspicious database is taken as input and by using the private key
K (same as that used for watermark information embedding) the embedded watermark is extracted if present and
then compared with the original watermark. Relational database watermarking approaches are application-
specific rather than being generalized which means that there is no general algorithm that can be applied to all
databases. Normally the characteristics of a watermarking algorithm are tied to the application it was actually
designed for.
II. Literature Survey
"Watermarking" is the process of hiding information into another data; the hidden information does not
need to have a relation with the data which is containing the information. [3] A digital watermark is a kind of
mark or information inserted into any data such as audio or image. It is basically used to identify ownership of
the data. Watermarks are generally used to verify the authenticity or integrity of the data or to show the identity
of its owners.
2.1. Applications Of Watermarking [3]:
2.1.1 Copyright protection: Watermarking is initially born to provide the copyright protection for the media
contents. The copyright information embedded into the data should survive all kinds of attacks either intentional
or unintentional.
2.1.2 Transaction tracking or fingerprinting: It also requires the embedded watermark should be robust
enough against malicious attacks.
2.1.3 Content Annotation: for this purpose the digital watermark is embedded to identify the producers and
provide his contact address.
2. Image Based Relational Database Watermarking: A Survey
DOI: 10.9790/0661-17315465 www.iosrjournals.org 55 | Page
2.1.4 Content authentication: It prevents the attackers from tampering the digital contents or the data. This
application is also known as fragile watermarking, which detects any form of changes even if one bit is
converted.
2.2 Classification Of Watermarking Techniques [3]: The watermarking techniques proposed so far can be
classified along various dimensions as follows:
2.2.1 Watermark Information: Different watermarking schemes embed different types of watermark
information into the underlying data of the database for e.g. image, text etc.
2.2.2 Distortion: Watermarking schemes may be distortion-based or distortion free depending on whether the
marking introduces any distortion to the data into which it is inserted.
2.2.3 Cover Type: Watermarking schemes can be classified based on the type of the cover (e.g. type of
attributes) into which mark information is embedded.
2.2.4 Granularity Level: The watermarking can be performed by modifying or inserting information at bit level
or higher level (e.g. character level or attribute level or tuple level).
2.2.5 Verifiability/ Detectability: The detection/verification process may be deterministic or probabilistic in
nature, it can be performed blindly or non-blindly, it can be performed publicly (by anyone) or privately (by the
owner only).
2.2.6 Intent of Marking: According to the purpose to be served watermarking schemes can be, namely,
integrity and tamper detection, localization, ownership proof, traitor detection etc.
2.3 Characteristics Of Watermarked Database [2]: The desired characteristics for watermarking relational
databases are:
2.3.1 Detectability: The watermark should be such it can be easily detected by the owner by examining
thetuples of the suspicious database.
2.3.2 Robustness: The capability of the watermarking scheme should be capable to survive intentional
(modifying, adding, deleting the tuples of database) as well as unintentional attacks (digital reproduction and
photocopying). Even if the database is modified the watermark should be detectable.
2.3.3 Capacity: The watermarking scheme should be such that maximum watermark information can be
embedded into the database.
2.3.4 Updatability: The watermark scheme should be such that the tuples of the relational database either
inserted or deleted; the watermark can withstand the change.
2.4 Different Types Of Attacks On Database [3]: The different types of attacks which can be done to destroy
the database are:
2.4.1 Benign Update: Modifying the original data without prior permission of owner. The watermarking should
be such that even after the modification the watermark isn‟t lost.
2.4.2 Value Modification Attack: In this type of attack the watermark is destroyed by altering one or more bits
in the watermarked database. For example an attempt of destroying watermark can be made by rounding all the
values of numeric attributes.
2.4.3 Subset Attack: By deleting or updating some of the tuples (subset) of the database attacker may try to
destroy the watermark
2.4.4 Superset Attack: Some new attributes or tuples are added into the database.
2.4.5 Subset Reverse Order Attack: By changing the order or position of the tuples the attacker try to erase or
disturb the watermark
3. Image Based Relational Database Watermarking: A Survey
DOI: 10.9790/0661-17315465 www.iosrjournals.org 56 | Page
2.4.6 Brute Force Attack: It is hit and trail method to destroy the watermark from the database.
III. Analysis Of Different Image Based Relational Database Watermarking Schemes
The first method for relational database watermarking was proposed by Agrawal and Kiernan, in which the
technique used, was to marks only numeric attributes with one-bit watermark scheme. [1] In image based
watermarking, image is used as the watermark information. There are various image based watermarking
schemes been suggested till now few of them are discussed here.
(i). Algorithm proposed by Zhang Yong et al., NIU Xia-mu et al., WU Di et al., Zhao Liang et al., LI Jun-
cao et al., XU Wei-jun et al.[11]
In this algorithm, Zang used patchwork algorithm to choose random pairs of points(ax. by) of any image in
spectral domain. They then increased the brightness at ax by 1 unit and then decreasing by‟s brightness and
inserted the processed image information to the attributes which can tolerate some errors.
Watermark insertion algorithm:
1) Read the size of image(m×n)
2) From the left to the right and from the top to the bottom, read the value of each pixel‟s RGB from
Image and obtain an ordered dataset, noted RGBValue(i,j) as embedding marks, x = 0…m×n-1, y = 0, 1,2(
the value of y denoted R, G and B of each pixel correspondingly)
3) Watermarking attributes Ak(k = 1…t );
4) RDB.FIRST;
5) While not RDB.EOF do
6) Begin
7) For k:=1 to t do
8) if HASH(Key°PK°Ak)mod X = 0; //PKdenotes the primary key of the current tuple
9) Begin
10) s = HASH(Key°PK°Ak)mod (m×n);
11) r = HASH(Key°PK°Ak)mod 3;
12) Embedding the value of RGBValue(s,r) into the attribute Ak of the current tuple;
13) End
14) Else no operation;
15) RDB.next;
16) End;
Watermark extraction algorithm:
1) Select the embedded mark attributes from the suspicious relational database, noted Ak (k = 1…t);
2) Initialize a (x,y), aCount(x,y), the initialized value is 0, x =0…m×n-1, y =0, 1,2;
3) RDB.first;
4) While not RDB.EOF do
5) Begin
6) for k:=1 to t do
7) if HASH(Key°PK°Ak) mod X = 0 //PK denotes the primary key of the current tuple
8) begin
9) s = HASH(Key°PK°Ak) mod (m×n);
10) r = HASH(Key°PK°Ak)mod 3;
11) Extract the mark from the current attribute Ak of the current tuple, added into x(s,r);
12) aCount(s,r) = aCount(s,r)+1;
13) end;
14) else no operation;
15) RDB.next;
16) End;
17) for x:=0 to m×n-1 do
18) for y:=0 to 2 do
19) begin
20) if aCount(x,y)<>0 then
21) a(x,y)= x(x,y)/ aCount(x,y);
22) According to the values of a(x,y), generating an image, the size of the image is m×n and the R, G and B
value of each pixel equals the values of a(x,y) corresponding;
23) end;
4. Image Based Relational Database Watermarking: A Survey
DOI: 10.9790/0661-17315465 www.iosrjournals.org 57 | Page
In the algorithm, among three attack modes, for the subset deleted attack, the method is the strongest
robustness, and for the subset modified attack, the robustness of the method is common, and for the added
attack, the robustness of the method is weaker.
(ii). Algorithm proposed by Zhi-Hao Zhang et al., Xiao-Ming Jin et al., Jian-Min Wan et al., De-Yi Li et
al. [12]
In this algorithm, they consider a pixel of image as a little bit error in database. All pixels express an
integrated copyright image. They assumed that the database contains float attribute, the number of tuples must
he greater than the numbers of image pixels. They then watermarked a database of relation R whose schema is
R (K. Ao, A1 ...A n), and K is the primary key of database, and it is never marked. The pixel values of an image
are I(vo, vi.. . . . .v,). “m” is great less than n. I: Ai denotes the value of attribute Ai in tuple r6R. The relation R
is divided into chunks of uniform size as same as the size of the image. Each chunk is regarded as a
watermarking cell. Having lowered the planar image dimension, the pixel values of the image could he embed
into the corresponding location of attributes. Pixel value and an attribute value of a tuple in relation are
compared. According to the pixel values and the embedding rule, the corresponding attribute values can he
marked.
Watermark Insertion Algorithm:
for each tuple re R do
if vi =255 then
mark (r. Ai mod 3)=1;
elseif vi =O then
mark (r. Ai mod 3)=2;
elseif(vi!=O and vi!=255) then
mark (r. Ai mod 3)+,
r. Ai =int(r. Ai) + unitary();
end;
end;
Watermark Detection Algorithm:
for each tuple re R do
if (r. Ai mod 3)=1 then
elseif (r. Ai mod 3)=2 then
else
vi 255;
VI 3;
vi =(I. Ai -int(r. Ai))*255;
imshow(vi);
end:
end;
The above algorithm suggested is easy and very effective. The algorithm is tested against various attacks
such as: subset addition attack, sunset out off order attack, subset selection attack and subset alteration attack. It
is found that the suggested algorithm is robust enough to different attacks.
(iii). Algorithm proposed by Jianhua Sun et al.,Zaihui Cao et al, Zhongyan Hu et al.[7]
This algorithm suggests a novel multiple watermarking scheme, which embeds two image as watermark
information into relational database.
Watermark insertion algorithm:
// Insert a plain watermark W into relation R in form of 0,1 sequences, return marked R
// The parameters k$, L, , and x are all private to the owner.
1) calculate L-bit EMC E[L]=H(k$ concatenate W)
// L is the length of watermark
2) foreach tupler R do
3) t= H(k$ concatenate r.P_keyi)
4) if ( t mod x equals 0) then // mark this tuple
5) attribute_ index i = t mod x // mark attribute Ai
6) bit_index j = t mod x // mark j th bit
5. Image Based Relational Database Watermarking: A Survey
DOI: 10.9790/0661-17315465 www.iosrjournals.org 58 | Page
7) watermark index k= t mod L //use the k-th bit of 0,1 sequences to mark
8) mark_bit m=E k XOR (k mod 2) // get the value of marked bit
9) set the j-th least significant bit of r.Ai to m
10) if (not within_usability(new_data)) // check the availability
11) rollback
12) else commit
13) return R
Watermark Extraction Algorithm
// Algorithm to return a watermark M[ ] from relation R
// parameters k, L, p, q and u are also private to the owner.
1 for s=0 to L-1 do
2 DM[s]=‟ ‟ // initialize the detected mark code
3 count[s][0]=0, count[s][1]=0 // initialize counter
4 for each tuple r_R do
5 t= H1( k concatenate r.P)
6 if ( t mod x equals 0) then // select this tuple
7 i = select_attribute() // mark i-th attribute
8 j = t mod q // select j-th bit
9 k= t mod L // mark the k-th bit of EMC
10 m= ( j-th LSB of r.Ai) XOR (k mod 2)
11 count[k][m]=count[k][m]+1 // add the counter
12 for s=0 to L-1 // get the watermark
13 if (count[s][0]>=count[s][1]) // majority voting
14 then M[s]=0 else M[s]=1 //the final bit value
The algorithm is “blind” in that it requires neither the original data nor the watermark in order to detect a
watermark in an object. The proposed method of watermarking relational databases using character images is
correct, feasible, and robust. The approach used here is more intuitive, and it support easy watermark
identification.
(iv). Algorithm proposed by Ashraf Odeh et al. and Ali Al-Haj et al. [5]
This algorithm suggests an efficient database watermarking algorithm based on inserting a binary image
watermark in the 'time' attribute of database tuples. The 'Time' attributes exit by default, but in most applications
they're not used. To be specific, the 'Date' attribute in databases is made of two fields: 'Date' and 'Time'. „Time'
field which is made of three fields: hours, minutes and seconds (HH:MM:SS). Hiding the binary information of
the watermark in the seconds field (SS) should have the least effect on the usability of the database. Basic
reason behind using time attribute is the large bit-capacity available for hiding the watermark information, and
thus large watermarks can be easily hidden.
Watermark insertion algorithm:
1: Transfer the image into a flow of bits.
2: Group every 5 bits as a binary string,
3: Find the decimal equivalent of the string
4: Embed the decimal number in tuples selected
by the pre-defined key 'K' as follows:
for each selected tuple do
for each selected 'Time' attribute do
if the 'SS' field of the 'time' mode K = 0
embed the decimal number
else Next attribute
end if
end loop
end loop
Watermark Extraction Algorithm
1. Extract the decimal number in tuples selected by the pre-defined key 'K' as follows:
for each selected tuple do
for each selected 'Time' attribute do
6. Image Based Relational Database Watermarking: A Survey
DOI: 10.9790/0661-17315465 www.iosrjournals.org 59 | Page
if the 'SS' field of the 'time' mode K = 0
extract the decimal number
else Next attribute
end if
end loop
end loop
2: Find the binary equivalent of the extracte decimal number.
3: Group every 5 bits as a binary string.
4: Reconstruct the binary image watermark from the binary strings.
A major advantage of using the time-attribute in database watermarking is the large bit-capacity available
to hide watermarks in the database. This is opposite to the more common bit-level database watermarking
algorithms where watermark bits have limited potential bit-locations that can be used to hide them without being
subjected to removal or destruction. The robustness of the proposed algorithm was verified against a number of
database attacks such subset deletion, subset addition, subset alteration and subset selection attacks.
(v). Algorithm proposed by Zhongyan Hu et al., Zaihui Cao et al., Jianhua Sun et al. [13]
In this approach, an identification image is embedded into the relational data for representing the copyright
information. It is assumed that some minor changes of some attributes values can be tolerated. Copyright
information is embedded into these attributes. It is considered that the character image (copyright information)
is a sequence of 0 and 1, the marks of 0 and 1 are small errors in the relational data. All the marks of 0 and 1
represent integrated copyright information. A character image which will convert as a sequence of 0 and 1 is to
be embedded into relation R for the purpose of copyright protection.
Watermark insertion algorithm:
// Insert a plain watermark W into relation R in form of 0,1 sequences, return marked R
// The parameters k$, L, γ, and ⱴ are all private to the owner.
1) calculate L-bit EMC E[L]=H(k$ concatenate W)
// L is the length of watermark
2) foreach tupler R do
3) t= H(k$ concatenate r.P_keyi)
4) if ( t mod γ equals 0) then // mark this tuple
5) attribute_ index x = t mod ⱴ // mark attribute Ax
6) bit_index y = t mod ξ // mark y th bit
7) watermark index k= t mod L //use the k-th bit of 0,1 sequences to mark
8) mark_bit m=E k XOR (k mod 2) // get the value of marked bit
9) set the y-th least significant bit of r.Ax to m
10) if (not within_usability(new_data)) // check the availability
11) rollback
12) else commit
13) return R
Watermark Extraction Algorithm:
// Algorithm to return a watermark M[ ] from relation R
// parameters k, L, α, ξ and ⱴ are also private to the owner.
1 for s=0 to L-1 do
2 DM[s] =‟‟ // initialize detected mark code
3 count[s][0]=0, count[s][1]=0 // initialize counter
4 for each tuple r_R do
5 t= H1 ( k concatenate r.P)
6 if (t mod γ equals 0) then // select this tuple
7 x = select_attribute() // mark x-th attribute
8 y = t mod ξ // select y-th bit
9 k= t mod L // mark the k-th bit of EMC
10 m= (y-th LSB of r.Ai) XOR (k mod 2)
11 count[k][m]=count[k][m]+1 // add the counter
12 for s=0 to L-1 // get the watermark
13 if (count[s][0]>=count[s][1]) // majority voting
14 then M[s] =0 else M[s]=1 //the final bit value
7. Image Based Relational Database Watermarking: A Survey
DOI: 10.9790/0661-17315465 www.iosrjournals.org 60 | Page
The suggested algorithm is tested against 3 major attacks namely, subset addition, deletion and alteration
attack. By the different attack models and the corresponding analysis, it is shown that the proposed method of
watermarking relational databases using character image is correct, feasible, and robust.
(vi). Algorithm proposed by Hossein Moradian Sardroudi et al., Subariah Ibrahim et al. [4]
In this approach an image as watermark information is embedded into numerical attributes of relational
database. It refers to image as 2-dimension matrix and tries to embed medium size image in small scale relation.
By applying this method upper Correction Factor for image can be obtained. Furthermore at the phase of
embedding watermark, this algorithm assures the minimum modification to original database without decreasing
imperceptibility by minimizing data variation. In extracting process majority voting method is used to retrieve
the correct watermark. Recovering step is added to the extracting process to improve this process. This step tries
to recover extracted watermark and makes a guess for missing elements value, so that watermark can be
detected even in a small subset of a database. Detecting the watermark does not require the original database and
the watermark. Therefore the approach is blind.
Watermark insertion algorithm:
Inputs: M, KEY, R, #LSB, A, #MSB, F
BEGIN
1. WM = Get_Watermark(M); //transfer image pixel into a matrix
2. FOR all tuples in relation LOOP
3. vpk = Hash_Primary_Key(pk, KEY); //calculate virtual primary key
4. IF MOD(vpk, F) = 0 THEN
5. a# = MOD(vpk, #A) + 1; //select one attribute for embedding
6. b# = MOD(vpk, #LSB) + 1; //select one of LSB bits for embedding
7. IF Binary Length(a ) > (2 * #LSB) THEN //check for attribute value tolerable for embedding
8. h= HASH(vpk, ImageHeight, 1); //compute mark height position
9. w= HASH(vpk, ImageWidth), 2); //compute mark width position
10. MSB_BIT=Select_MSB_BIT(vpk,a,#MSB);//select one of MSB bits
11. a‟(b#) = WM(h)(w) XOR MSB_BIT; //XOR corresponding mark bit with selected MSB bit
12. a‟ = MinimizeVariation(a, a‟ , b#, #LSB ); //minimize attribute value variation
13. Update Table(R, pk, a#, a‟);
14. END IF;
15. END IF;
16. END LOOP
17. IF NO ERROR THEN
18. COMMIT;
19. ELSE
20. ROLLBACK;
21. END IF;
END;
Watermark Extraction Algorithm:
Inputs: #MSB, Image_Height, KEY, R, A, #LSB, F, Image_Width.
BEGIN
1. FOR all tuples in relation LOOP
2. vpk = Hash_Primary_Key(pk, KEY); //calculate virtual primary key
3. IF MOD (vpk, F) = 0 THEN
4. a# = MOD(vpk, #A) + 1; //select one attribute for embedding
5. b#= MOD(vpk, #LSB) + 1;//select one of LSB bits for embedding
6. IF Binary Length(a ) > (2*#LSB) THEN //check for attribute value tolerable for embedding
7. h = HASH(vpk, ImageHeight, 1);//compute mark height position
8. w = HASH(vpk, ImageWidth), 2);//compute mark width position
9. MSB_BIT=Select_MSB_BIT(vpk,a,#MSB);//select one of MSB bits
10. WM (h)(w) = a(b#) XOR MSB_BIT; //XOR corresponding extracted mark bit with selected MSB bit
11. END IF;
12. END IF;
13. END LOOP
14. IF NO ERROR THEN
8. Image Based Relational Database Watermarking: A Survey
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15. M=Generate_Watermark(WM);//transfer matrix elements into image
16. Recover_Image(M);
17. ELSE
18. Message (Error_Code);
19. END IF;
END;
This paper illustrated the new approach for watermarking relational database. It presented a resilient
watermarking technique for relational database that embeds image bits in the small size database as the
watermark. It is robust to the important attacks
(vii). Algorithm proposed by Udai Pratap Rao et al., Dhiren R. Patel et al., Punitkumar M. Vikani et al.
[6]
This paper suggests a technique for relational database watermarking which uses binary image as
watermark information. The image bits are inserted into database, it represents the copyright information. The
overall variation in the watermarked database is also minimized so that distortion is less.
Watermark insertion algorithm:
// the algorithm inserts a watermark information into the original database D, and return marked D.
// the parameters M, F,
1. Convert an image (m x n) into matrix of 0 & 1, and store this matrix into W[m][n].
2. For each tuple r in D do
3. t = HASH(Ks concat r.P)
4. if (t mod F == 0) then // this tuple is available for marking
5. attribute_index x = t mod v // mark attribute Ax
6. x th bit
7. select row of an image a = (x * v) mod m
8. watermark_index k = t mod length(a) // it gives some bit position in ath row of watermark(image)
9. h = (HASH(t concat k(row value))) mod m // h is the position for selected mark bit from M
10. w = (HASH(t concat k(col value))) mod n // w is the position for selected mark bit from M
11. Replace the jth LSB of r.Ax with W[h][w] bit
12. Now, apply the minimize variation
13. Update D;
14. End loop;
Watermark extraction algorithm:
//two integer parameters are used, total_count=0 (to count the total no of watermarked bits) and match_count=0
(to count the total no of matched watermarked bits).
1. Convert an image (m x n) into matrix of 0 & 1, and store this matrix into W[m][n].
2. For each tuple r in D do
3. t = HASH(Ks concat r.P)
4. if(t mod F == 0) then // select this tuple
5. attribute_index x = t mod v // mark attribute Ax
6. bit_inde th bit
7. select row of an image a = (x * v) mod m
8. watermark_index k = t mod length(a) // it gives some bit position in ath row of watermark(image)
9. h = (HASH(t concatenate k(row value))) mod m // h is the position for selected mark bit from M
10. w = (HASH(t concatenate k(col value))) mod n // w is the position for selected mark bit from M
11. total_count++;
12. if W[h][w] matched with jth LSB
13. match_count++;
14. End if;
15. End loop;
16. if (match_count / total_count>= a)
17. Has watermark
The proposed technique minimizes the variation by inverting some bits of the watermarked attribute. That
proposed technique is robust irrespective to the tuples order
(viii). Algorithm proposed by Ying Wang et al., Geng-Ming Zhu et al., Shao-Bo Zhang et al. [10]
9. Image Based Relational Database Watermarking: A Survey
DOI: 10.9790/0661-17315465 www.iosrjournals.org 62 | Page
This paper presents a watermarking algorithm which is based on numerical attribute in the relational
databases. The watermark information is embedded by using the Arnold transformation and scrambling
technology, the parity of the low decimal number of numeric attribute is modified, connected with some
attribute in the physical storage space in the relational databases.
Watermark insertion algorithm:
1. Identify a number of secret information, the image watermark w, the user‟s watermarking key user _ key, the
controlling factor ω, the embedded factor γ
Wt = {Wt (x)|W(x) {0,1}, 0 ≤ i ≤ m× m} //to get one dimensional vector, m×m is size of watermark image
3: identify the number of numeric attributes of the embedded watermarking as v, and arrange the primary key
and numeric attributes by order in the relational database;
4. Set the searching function as find()//If the function value returns to be true , then the candidate attribute
values rx.Ay can be embedded watermarking
5. id =hash(user _ key,P)
6. To determine the embedding watermarking tuples ri according to the embedding watermarking factorγ, using
the mod as the taking over function that satisfy if (id mod 1 /γ == 0);
7: To determine the watermarking attributes of the tuples ri.Aj that is candidate attribute value ri.Aj according to
the remainder of (id mod v)
8: To determine the low position of the decimal j d of the attribute value rx .Ay according to the control factor ω ,
and seek the candidate parity bits of rx.du , that is par (x ) = rx.dy mod 2, par(x)= {0,1};
9. sta(x) Statisticw(rx .P)mod 2 and sta(x) = {0,1};
10: To obtain x = id mod l according to id of the tuples rx, and determine the corresponding position x of the
watermarking according to the remainder;
11: wvalue = addmark (w (x), sta (x), par (x));
12: Then to modify the rx.dy value as wvalue;
13: Return to Setp6, until all the one-dimensional watermarking vectors w (x) can be embedded in the candidate
attributes;
Watermark extraction algorithm:
1. Identify a number of secret information: the image watermark w, the users‟ watermarking key, user _ key,
control factorω , embedded factor γ ;
2. Arrange the primary key in the relational database and numeric attributes by order;
3. Search the relational database in detection, and find out the candidate tuples attributes ri.Aj (1 ≤ x ≤ n, 1 ≤ y
≤ v) x y that can be embedded watermarking within the permissible data error δy. Use find () in embedding
operation to complete the process.
4. To restore the marks id of the numeric tuples, that is to recalculate the hash value.
5. To find out the tuples rx that is embedded watermarking according to the watermarking embedding factor γ.
That is to select the candidate tuple attribute values rx.Ay and identify the tuple watermarking embedding
position as long as if (id mod 1 == 0), and the watermark embedding attributes Ay is determined;
6. Determine the low position in decimal of the embedding watermarking attributes rx .Ay according to the
control factor ω ;
7. w(x)=holdmark(par(x) , sta(y))
8. Determine the watermarking position x corresponding to the watermarking signal w (x) according to x = id
mod l;
9. Back to Step5 to find the next relational databasetuple that is embedded watermarking, until all the
embedded watermark tuples are found out.
10. To do the majority of the election on the watermarking signal w (x) in the same extracted watermarking
position x;
11. After get a one-dimensional watermarking vector – W‟t, W‟t = { W‟t (x) | W‟(x) {0,1}, 0≤ x ≤ m×m } to
map W‟t into a two dimensional Matrix W' ,W′={w′(x, y)|w′(x, y) ∈{0,1},1≤ x ≤ m,1 ≤ y ≤ m}according to
the line scan sequence, which is the restored watermarking signal;
12. To express the black and white pixel values of
W' with the actual value and to restore the binary image D' from the database, and then get the original image
from
T − C times Arnold transforming and restoring.
The above algorithm, image has the stronger robustness. By taking double filtering mechanism to
determine the location of the watermarking embedded can make the watermark embedding much safer and more
subtle. By modifying the parity of the numerical attribute of the low value of to embed watermarking, rather
10. Image Based Relational Database Watermarking: A Survey
DOI: 10.9790/0661-17315465 www.iosrjournals.org 63 | Page
than directly operating against the physical storage of data-bit, the range of data modification and amount of
computing is smaller, and the operation is much easier. The algorithm meets the synchronization requirements
of the database dynamically update it is blind watermarking algorithm.
(ix) Algorithm proposed by Yi Liu et al., Juan Wang et al. [9]
The algorithm scans candidate attributes where watermark can be embedded and then conduct subset
segmentation and rearrangement, and then DWT transformation is performed to the data subsets and the
scrambled watermark image respectively. The compressed low-frequency part of the watermark is embedded
into the High-frequency part of the data set to achieve data fusion.
Watermark insertion algorithm:
1) Perform K times of Arnold scrambling to a M×N binary image W and W becomes W'. W' =
{W'(x,yj)|0≤x≤M, 0≤y≤N}, and preserve scrambling times k as the key. Execute the three level wavelet
decomposition to the scrambled image and get the wavelet coefficients matrix of the third-level lowfrequency
subblock LL3. The mean of the coefficient matrix is calculated and labelled as Avg and save it as a key, and
then each coefficient of the matrix minus Avg to come in for the compressed low-frequency subblock LL3'.
2) Utilizing the algorithm mentioned in section 3.1.2 screen the candidate attributes that can be embedded
watermark and using label algorithm mark the Aj, IDrx.Ajy= hash(Key, P, Ay).
3) Grouping the data into λ packets according to the values that come from the labeled ID mod λ(ID % λ).
Group (k) =IDrx.Ay Mod λ{0≤k≤λ-1}, where λ is the repeating times of watermark embedding and its value can
be set in accordance with the specific relationship and the watermark information.
4) Sort the data in each packet according to the ID value, there are total M × N bits in each packet (the
watermark length) and fill them with 0 if void bits appeared.
5) Perform three level wavelet transformation to each Group (k)(0 ≤ k ≤ λ-1) respectively and get the third level
high-frequency subblock 3 HHk (0 ≤ k ≤λ −1) .
6) By way of adding embed the compressed watermark low-frequency coefficients LL3' into the high-frequency
subblocks HH3'(1≤k≤λ-1) of the host data. The specific embedding mode as follows: HH3 k =HH3 k(1+αLL3'),
(0≤k≤λ-1) where α represents the intensity factor of high-frequency subband watermark embedding.
7) Conduct the inverse transformation to the watermarked coefficients and get the watermark contained data.
Watermark extraction algorithm:
Watermark detection is the reverse process of embedding, but needs to calculate the similarity between the
extracted watermark signal W* with the original watermark W. If the correlation coefficient Sim is greater than
the threshold T, then the watermark exists and not exists otherwise.
Sim = WT
* W*
, 0 < Sim > 1
W T W W ∗ T W ∗
Algorithm using wavelet transformation skill embeds the compressed "small" watermark to a relative
"large" host database. Not only has little influence on the database but also greatly reduces the probability of
watermark damage, which effectively overcomes the defect that spatial algorithms are usually produce morbid
results. The experimental results reveal that the complexity of algorithm is simple, has perfect invisibility and
strong resistance to varied attacks, and especially enjoys sturdy immunity for subset modification and subset
deletion.
(x) Algorithm proposed by Ramani Sagar V. [8]
In the algorithm, ownership verification of a database by inserting an imperceptive watermark in such a
way to provide robustness and security against attempts to remove the watermark. To prove ownership of the
database watermarking is done using image. Image is converted into the row bits and row bits is encrypted using
MD5 security algorithm .This row bits will be embed into the database attribute in terms of watermark.
Watermark insertion algorithm:
1) Take a path for a given database to be watermarked
2) Based on database count no of tuples & no of columns to be watermarked
3) Obtain secret key Sk for MD5 hashing
4) For MD5 hashing obtain primary key P and concatenate with secret key Sk
11. Image Based Relational Database Watermarking: A Survey
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5) Using MD5 algorithm obtain hash value using combination of P+Sk
6) Mark the tuple based on the hash value If(H_hashvalue mod
F= 0) then mark the tuple Where F= database partitioning value which will be private to the database owner
7) Obtain attribute indexA based on (H_hashvalue mod V =0)
Where V =No of columns of the database
8) Obtain bit indexB which will be marking a particular bit of attribute based on(H_hashvalue mod ξ= 0) where
ξ=private to the owner
9) Select the image bit procedure for the given image bases on hash value generated by the MD5
Select row of image based on (A*V) mod H where H = image height
Select column of image based on H_hashvalue mod W where W = image width
Convert row value into decimal value
Convert column value into decimal value
Obtain height & width for marked bit from the image based on hashing algo MD5 concatenated value of
row
Select bit of image for marking based on marked bits
10) Select field from database based on hash function
11) Covert attribute field into the binary field
12) Replace in LSB of binary value of attribute with selected image bit
13) Replace new attribute value in the database
Watermark extraction algorithm:
1) Take a path for a given database
2) Initially totalcount=matchcount=0
3) Obtain MD5 hashing obtain hash value using P+Sk
4) Detect the tuple based on the following condition
IF (H_hashvalues mod F=0)then mark the tuple
Where F=data partitioning value which will be private to the owner
Obtain attribute index i = (H_hashvalue mod V=0) where V =No of columns of the database
Obtain bit index B which will be marking a particular bit of the attribute based on (H_hashvalue mod ξ=0 )
where ξ=private to owner
If(bit index B<=length of selected field ) then
totalcount++
matchcount=matchcount+match(selected field ,bit index, selected bit)
In the given algorithm, each tuple in a table is independently processed; therefore, the scheme is
particularly efficient for tuple oriented database operations. This watermarking scheme is robust for two attacks
which are attribute addition attacks and subset reverse order attack.
IV. Conclusion
In this paper we survey the current state-of-the-art of different image based watermarking techniques for
relational databases. We can insert an image as watermark in our database in various ways. It should be noted
that all the techniques of watermarking database can only be applied for real time copyright protection of the
database. These methods cannot prevent piracy or illegal copying of database. Methods of Watermarking of
relational databases are basically divided into two types, Distortion Based watermarking and Distortion Free
watermarking. Watermarking of Relational Database implemented by Image Based Watermarking is actually
Distortion Based Watermarking. After studying and analysing various research papers and approaches it can
assured that image based watermarking can be efficiently used for security and authentication of database.
Image based database watermarking is also useful for ownership protection. This technique will be useful for
fraud and temper detection also.
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