Distortion-Free Watermarking Approach for Relational Database Integrity Checking

Nowadays, internet is becoming a suitable way of accessing the databases. Such data are exposed to various types of attack with the aim to confuse the ownership proofing or the content protection. In this paper, we propose a new approach based on fragile zero watermarking for the authentication of numeric relational data. Contrary to some previous databases watermarking techniques which cause some distortions in the original database and may not preserve the data usability constraints, our approach simply seeks to generate the watermark from the original database. First, the adopted method partitions the database relation into independent square matrix groups. Then, group-based watermarks are securely generated and registered in a trusted third party. The integrity verification is performed by computing the determinant and the diagonal’s minor for each group. As a result, tampering can be localized up to attribute group level. Theoretical and experimental results demonstrate that the proposed technique is resilient against tuples insertion, tuples deletion, and attributes values modification attacks. Furthermore, comparison with recent related effort shows that our scheme performs better in detecting multifaceted attacks

Survey on relational database watermarking techniques

Digital watermarking has been in multimedia data use over the past years. Recently it has become applicable in relational database system not only to secure copyright ownership but also to ensure data contents integrity. Further, it is used in locating tampered and modified places. However, the watermarking relational database has its own requirements, challenges, attacks and limitations. This paper, surveys recent database watermarking techniques focusing on the importance of watermarking relational database, the difference between watermarking relational database and multimedia objects, the issues in watermarking relational database, type of attacks on watermarked database, classifications, distortion introduced and the embedded information. The comparative study shows that watermarking relational database can be an effective tool for copyright protection, tampered detection, and hacker tracing while maintaining the integrity of data contents. In addition, this study explores the current issues in watermarking relational database as well as the significant differences between watermarking multimedia data and relational database contents. Finally, it provides a classification of database watermarking techniques according to the way of selecting the candidate key attributes and tuples, distortion introduced and decoding methods used

Design and Analysis of an Intelligent Integrity Checking Watermarking Scheme for Ubiquitous Database Access

As a result of the highly distributed nature of ubiquitous database accessing, it is essential to develop security mechanisms that lend themselves well to the delicate properties of outsourcing databases integrity and copyright protection. Researchers have begun to study how watermarking computing can make ubiquitous databases accessing more confident work environments. One area where database context may help is in supporting content integrity. Initially, most of the research effort in this field was depending on distortion based watermark while the few remaining studies concentrated on distortion-free. But there are many disadvantages in previous studies; most notably some rely on adding watermark as an extra attributes or tuples, which increase the size of the database. Other techniques such as permutation and abstract interpretation framework require much effort to verify the watermark. The idea of this research is to adapt an optimized distortion free watermarking based on fake tuples that are embedded into a separate file not within the database to validate the content integrity for ubiquitous database accessing. The proposed system utilizes the GA, which boils down its role to create the values of the fake tuples as watermark to be the closest to real values. So that it's very hard to any attacker to guess the watermark. The proposed technique achieves more imperceptibility and security. Experimental outcomes confirm that the proposed algorithm is feasible, effective and robust against a large number of attacks

Localizing unauthorized updates in published micro-data tables through secret order-based watermarking

The study of micro-data disclosure issue has largely focused on the privacy preservation aspect, whereas the integrity of a published micro-data table has received limited attention. Unauthorized updates to such a table may lead users to believe in misleading data. Traditional cryptographic stamp-based approaches allow users to detect unauthorized updates using credentials issued by the data owner. However, to localize the exact corrupted tuples would require a large number of cryptographic stamps to be stored, leading to prohibitive storage requirements. In this thesis, we explore the fact that tuples in a micro-data table must be stored in a particular order, which has no inherent meaning under the relational model. We propose a series of algorithms for embedding watermarks through reordering the tuples. The embedded watermarks allow users to detect, localize, and restore corrupted tuples with a single secret key issued by the data owner, and no additional storage is required. At the same time, our algorithms also allow for efficient updates by the data owner or legitimate users who know the secret key. The proposed algorithms are implemented and evaluated through experiments with real data

Protection of Relational Databases by Means of Watermarking: Recent Advances and Challenges

Databases represent today great economical and strategic concerns for both enterprises and public institutions. In that context, where data leaks, robbery as well as innocent or even hostile data degradation represent a real danger, and watermarking appears as an interesting tool. Watermarking is based on the imperceptible embedding of a message or watermark into a database in order, for instance, to determine its origin as well as to detect if it has been modified. A major advantage of watermarking in relation to other digital content protection mechanisms is that it leaves access to the data while keeping them protected by means of a watermark, independent of the data format storage. Nevertheless, it is necessary to ensure that the introduced distortion does not perturb the exploitation of the database. In this chapter, we give a general overview of the latest database watermarking methods, focusing on those dealing with distortion control. In particular, we present a recent technique based on an ontological modeling of the database semantics that represent the relationships in between attributes—relationships that should be preserved in order to avoid the appearance of incoherent and unlikely records

A Fragile Zero Watermarking Scheme to Detect and Characterize Malicious Modifications in Database Relations

We put forward a fragile zero watermarking scheme to detect and characterize malicious modifications made to a database relation. Most of the existing watermarking schemes for relational databases introduce intentional errors or permanent distortions as marks into the database original content. These distortions inevitably degrade the data quality and data usability as the integrity of a relational database is violated. Moreover, these fragile schemes can detect malicious data modifications but do not characterize the tempering attack, that is, the nature of tempering. The proposed fragile scheme is based on zero watermarking approach to detect malicious modifications made to a database relation. In zero watermarking, the watermark is generated (constructed) from the contents of the original data rather than introduction of permanent distortions as marks into the data. As a result, the proposed scheme is distortion-free; thus, it also resolves the inherent conflict between security and imperceptibility. The proposed scheme also characterizes the malicious data modifications to quantify the nature of tempering attacks. Experimental results show that even minor malicious modifications made to a database relation can be detected and characterized successfully

Classifiers and machine learning techniques for image processing and computer vision

Orientador: Siome Klein GoldensteinTese (doutorado) - Universidade Estadual de Campinas, Instituto da ComputaçãoResumo: Neste trabalho de doutorado, propomos a utilizaçãoo de classificadores e técnicas de aprendizado de maquina para extrair informações relevantes de um conjunto de dados (e.g., imagens) para solução de alguns problemas em Processamento de Imagens e Visão Computacional. Os problemas de nosso interesse são: categorização de imagens em duas ou mais classes, detecçãao de mensagens escondidas, distinção entre imagens digitalmente adulteradas e imagens naturais, autenticação, multi-classificação, entre outros. Inicialmente, apresentamos uma revisão comparativa e crítica do estado da arte em análise forense de imagens e detecção de mensagens escondidas em imagens. Nosso objetivo é mostrar as potencialidades das técnicas existentes e, mais importante, apontar suas limitações. Com esse estudo, mostramos que boa parte dos problemas nessa área apontam para dois pontos em comum: a seleção de características e as técnicas de aprendizado a serem utilizadas. Nesse estudo, também discutimos questões legais associadas a análise forense de imagens como, por exemplo, o uso de fotografias digitais por criminosos. Em seguida, introduzimos uma técnica para análise forense de imagens testada no contexto de detecção de mensagens escondidas e de classificação geral de imagens em categorias como indoors, outdoors, geradas em computador e obras de arte. Ao estudarmos esse problema de multi-classificação, surgem algumas questões: como resolver um problema multi-classe de modo a poder combinar, por exemplo, caracteríisticas de classificação de imagens baseadas em cor, textura, forma e silhueta, sem nos preocuparmos demasiadamente em como normalizar o vetor-comum de caracteristicas gerado? Como utilizar diversos classificadores diferentes, cada um, especializado e melhor configurado para um conjunto de caracteristicas ou classes em confusão? Nesse sentido, apresentamos, uma tecnica para fusão de classificadores e caracteristicas no cenário multi-classe através da combinação de classificadores binários. Nós validamos nossa abordagem numa aplicação real para classificação automática de frutas e legumes. Finalmente, nos deparamos com mais um problema interessante: como tornar a utilização de poderosos classificadores binarios no contexto multi-classe mais eficiente e eficaz? Assim, introduzimos uma tecnica para combinação de classificadores binarios (chamados classificadores base) para a resolução de problemas no contexto geral de multi-classificação.Abstract: In this work, we propose the use of classifiers and machine learning techniques to extract useful information from data sets (e.g., images) to solve important problems in Image Processing and Computer Vision. We are particularly interested in: two and multi-class image categorization, hidden messages detection, discrimination among natural and forged images, authentication, and multiclassification. To start with, we present a comparative survey of the state-of-the-art in digital image forensics as well as hidden messages detection. Our objective is to show the importance of the existing solutions and discuss their limitations. In this study, we show that most of these techniques strive to solve two common problems in Machine Learning: the feature selection and the classification techniques to be used. Furthermore, we discuss the legal and ethical aspects of image forensics analysis, such as, the use of digital images by criminals. We introduce a technique for image forensics analysis in the context of hidden messages detection and image classification in categories such as indoors, outdoors, computer generated, and art works. From this multi-class classification, we found some important questions: how to solve a multi-class problem in order to combine, for instance, several different features such as color, texture, shape, and silhouette without worrying about the pre-processing and normalization of the combined feature vector? How to take advantage of different classifiers, each one custom tailored to a specific set of classes in confusion? To cope with most of these problems, we present a feature and classifier fusion technique based on combinations of binary classifiers. We validate our solution with a real application for automatic produce classification. Finally, we address another interesting problem: how to combine powerful binary classifiers in the multi-class scenario more effectively? How to boost their efficiency? In this context, we present a solution that boosts the efficiency and effectiveness of multi-class from binary techniques.DoutoradoEngenharia de ComputaçãoDoutor em Ciência da Computaçã

Introductory Computer Forensics

INTERPOL (International Police) built cybercrime programs to keep up with emerging cyber threats, and aims to coordinate and assist international operations for ?ghting crimes involving computers. Although signi?cant international efforts are being made in dealing with cybercrime and cyber-terrorism, ?nding effective, cooperative, and collaborative ways to deal with complicated cases that span multiple jurisdictions has proven dif?cult in practic