Digital watermarking is a key ingredient to copyright protection. It provides a solution to illegal copying of digital material and has many other useful applications such as broadcast monitoring and the recording of electronic transactions. Now, for the first time, there is a book that focuses exclusively on this exciting technology. Digital Watermarking covers the crucial research findings in the field: it explains the principles underlying digital watermarking technologies, describes the requirements that have given rise to them, and discusses the diverse ends to which these technologies are being applied. As a result, additional groundwork is laid for future developments in this field, helping the reader understand and anticipate new approaches and applications.

Digital Watermarking

Steganography is an ancient art. With the advent of computers, we have vast accessible bodies of data in which to hide information, and increasingly sophisticated techniques with which to analyze and recover that information. While much of the recent research in steganography has been centered on hiding data in images, many of the solutions that work for images are more complicated when applied to natural language text as a cover medium. Many approaches to steganalysis attempt to detect statistical anomalies in cover data which predict the presence of hidden information. Natural language cover texts must not only pass the statistical muster of automatic analysis, but also the minds of human readers. Linguistically naive approaches to the problem use statistical frequency of letter combinations or random dictionary words to encode information. More sophisticated approaches use context-free grammars to generate syntactically correct cover text which mimics the syntax of natural text. None of these uses meaning as a basis for generation, and little attention is paid to the semantic cohesiveness of a whole text as a data point for statistical attack. This paper provides a basic introduction to steganography and steganalysis, with a particular focus on text steganography. Text-based information hiding techniques are discussed, providing motivation for moving toward linguistic steganography and steganalysis. We highlight some of the problems inherent in text steganography as well as issues with existing solutions, and describe linguistic problems with character-based, lexical, and syntactic approaches. Finally, the paper explores how a semantic and rhetorical generation approach suggests solutions for creating more believable cover texts, presenting some current and future issues in analysis and generation. The paper is intended to be both general enough that linguists without training in information security and computer science can understand the material, and specific enough that the linguistic and computational problems are described in adequate detail to justify the conclusions suggested.

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Linguistic steganography: survey, analysis, and robustness concerns for hiding information in text

Protecting rights over relational data is of ever increasing interest, especially considering areas where sensitive, valuable content is to be outsourced. A good example is a data mining application, where data is sold in pieces to parties specialized in mining it.Different avenues for rights protection are available, each with its own advantages and drawbacks. Enforcement by legal means is usually ineffective in preventing theft of copyrighted works, unless augmented by a digital counter-part, for example watermarking.Recent research of the authors introduces the issue of digital watermarking for generic number sets. In the present paper we expand on this foundation and introduce a solution for relational database content rights protection through watermarking.Our solution addresses important attacks, such as data re-sorting, subset selection, linear data changes (applying a linear transformation on arbitrary subsets of the data). Our watermark also survives up to 50% and above data loss.Finally we present wmdb.*, a proof-of-concept implementation of our algorithm and its application to real life data, namely in watermarking the outsourced Wal-Mart sales data that we have available at our institute.

/pdf/rights-protection-for-relational-data-1ackkutftj.pdf

Rights protection for relational data

we introduce a solution for relational database content rights protection through watermarking. Rights protection for relational data is of ever-increasing interest, especially considering areas where sensitive, valuable content is to be outsourced. A good example is a data mining application, where data is sold in pieces to parties specialized in mining it. Different avenues are available, each with its own advantages and drawbacks. Enforcement by legal means is usually ineffective in preventing theft of copyrighted works, unless augmented by a digital counterpart, for example, watermarking. While being able to handle higher level semantic constraints, such as classification preservation, our solution also addresses important attacks, such as subset selection and random and linear data changes. We introduce wmdb., a proof-of-concept implementation and its application to real-life data, namely, in watermarking the outsourced Wal-Mart sales data that we have available at our institute.

/pdf/rights-protection-for-relational-data-5lywxu7i2z.pdf

Cryptology is the practice of hiding digital information by means of various obfuscatory and steganographic techniques. The application of said techniques facilitates message confidentiality and sender/receiver identity authentication, and helps to ensure the integrity and security of computer passwords, ATM card information, digital signatures, DVD and HDDVD content, and electronic commerce. Cryptography is also central to digital rights management (DRM), a group of techniques for technologically controlling the use of copyrighted material that is being widely implemented and deployed at the behest of corporations that own and create revenue from the hundreds of thousands of mini-transactions that take place daily on programs like iTunes.

This new edition of our best-selling book on cryptography and information hiding delineates a number of different methods to hide information in all types of digital media files. These methods include encryption, compression, data embedding and watermarking, data mimicry, and scrambling. During the last 5 years, the continued advancement and exponential increase of computer processing power have enhanced the efficacy and scope of electronic espionage and content appropriation. Therefore, this edition has amended and expanded outdated sections in accordance with new dangers, and includes 5 completely new chapters that introduce newer more sophisticated and refined cryptographic algorithms and techniques (such as fingerprinting, synchronization, and quantization) capable of withstanding the evolved forms of attack. 

Each chapter is divided into sections, first providing an introduction and high-level summary for those who wish to understand the concepts without wading through technical explanations, and then presenting concrete examples and greater detail for those who want to write their own programs. This combination of practicality and theory allows programmers and system designers to not only implement tried and true encryption procedures, but also consider probable future developments in their designs, thus fulfilling the need for preemptive caution that is becoming ever more explicit as the transference of digital media escalates.


* Includes 5 completely new chapters that delineate the most current and sophisticated cryptographic algorithms, allowing readers to protect their information against even the most evolved electronic attacks.

* Conceptual tutelage in conjunction with detailed mathematical directives allows the reader to not only understand encryption procedures, but also to write programs which anticipate future security developments in their design.

* Grants the reader access to online source code which can be used to directly implement proven cryptographic procedures such as data mimicry and reversible grammar generation into their own work.

Disappearing Cryptography: Information Hiding: Steganography and Watermarking

Two main results in the area of information hiding in natural language text are presented. A semantically-based scheme dramatically improves the information-hiding capacity of any text through two techniques: (i) modifying the granularity of meaning of individual sentences, whereas our own previous scheme kept the granularity fixed, and (ii) halving the number of sentences affected by the watermark. No longer a long text, short watermark approach, it now makes it possible to watermark short texts, like wire agency reports. Using both the above-mentioned semantic marking scheme and our previous syntactically-based method hides information in a way that reveals any non-trivial tampering with the text (while re-formatting is not considered to be tampering-the problem would be solved trivially otherwise by hiding a hash of the text) with a probability 1-2 -β(n+1) , n being its number of sentences and β a small positive integer based on the extent of co-referencing.

Natural language watermarking and tamperproofing

Two main results in the area of information hiding in natural language text are presented. A semantically-based scheme dramatically improves the information-hiding capacity of any text through two techniques: (i) modifying the granularity of meaning of individual sentences, whereas our own previous scheme kept the granularity fixed, and (ii) halving the number of sentences affected by the watermark. No longer a "long text, short watermark" approach, it now makes it possible to watermark short texts, like wire agency reports. Using both the above-mentioned semantic marking scheme and our previous syntactically-based method hides information in a way that reveals any non-trivial tampering with the text (while re-formatting is not considered to be tampering--the problem would be solved trivially otherwise by hiding a hash of the text) with a probability 1-2-s (n+1), n being its number of sentences and s a small positive integer based on the extent of co-referencing.

/pdf/natural-language-watermarking-and-tamperproofing-3krvc0x495.pdf

Natural Language Watermarking and Tamperproofing

• Inclusion of natural language (NL) data sources as an integral part of the overall data sources in InfoSec applications • Analysis of NL at the level of meaning with the knowledge-based methods ontological semantics

Mercan Karahan

Papers

Natural language watermarking and tamperproofing

Natural Language Watermarking and Tamperproofing

Natural language information assurance and security