Electronic marking and identification techniques to discourage document copying

Brassil, Jack; Low, Steven H.; Maxemchuk, Nicholas F.; O’Gorman, Lawrence

doi:10.1109/49.464718

Cited by 284 publications

(68 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In word shifting method [30], the words are moved horizontally making spaces expanding between words to hide information. This method needs the presence of the original file or the original image due to the possibility that the file contains a variable number of spaces between adjacent words.…”

Section: Word-shift Codingmentioning

confidence: 99%

See 1 more Smart Citation

Arabic Text Watermarking : A Review

Alotaibi¹,

Elrefaei²

2015

IJAIA

View full text Add to dashboard Cite

show abstract

Section: Word-shift Codingmentioning

confidence: 99%

“…This approach modifies features a little bit to embed a watermark. In [30] the length of end lines of letters, b, d, h, etc is altered by extending or shortening it. Line and word shifting could be applied in any language even Arabic but the feature coding is done on a language based on its features.…”

Section: Feature Codingmentioning

confidence: 99%

Arabic Text Watermarking : A Review

Alotaibi¹,

Elrefaei²

2015

IJAIA

View full text Add to dashboard Cite

show abstract

“…Spread spectrum has been deployed in many researches to strengthen the robustness of the watermark. There are many researches of robust watermarking techniques (Bender, Gruhl, & Morimoto, 1995;O'Ruanaidh & Pun, 1998) and watermarks for other media such as motion picture (Echizen, Yoshiura, Arai, Himura, & Takeuchi, 1999) and text data (Brassil, Low, Maxemchuk, & O'Gorman, 1994;Brassil & O'Gorman, 1996).…”

Section: General Background Surrounding Digital Watermarksmentioning

confidence: 99%

Damageless Watermark Extraction Using Nonlinear Feature Extraction Scheme Trained on Frequency Domain

Naoe

Takefuji

2008

Intellectual Property Protection for Multimedia Information Technology

View full text Add to dashboard Cite

IntroductIonIn this chapter, we present a new model of digital watermark that does not embed any data into the content, but is able to extract meaningful data from the content. This is done by processing the coefficients of the selected feature subblocks to the trained neural network. This model trains a neural AbstrActIn this chapter, we propose a new information hiding and extracting method without embedding any information into the target content by using a nonlinear feature extraction scheme trained on frequency domain. The proposed method can detect hidden bit patterns from the content by processing the coefficients of the selected feature subblocks to the trained neural network. The coefficients are taken from the frequency domain of the decomposed target content by frequency transform. The bit patterns are retrieved from the network only with the proper extraction keys provided. The extraction keys, in the proposed method, are the coordinates of the selected feature subblocks and the neural network weights generated by the supervised learning of the neural network. The supervised learning uses the coefficients of the selected feature subblocks as the set of input values, and the hidden bit patterns are used as the teacher signal values of the neural network, which is the watermark signal in the proposed method. With our proposed method, we are able to introduce a watermark scheme with no damage to the target content.,

show abstract

“…In image-based approach, a watermark is embedded in text image. Brassil, et al were the first to propose a few text watermarking methods utilizing text image [4,5]; they also developed document watermarking schemes based on line shifts, word shifts as well as slight modifications to the characters [6]. Maxemchuk, et al [23,24,25] analyzed the performance of these methods, while later Low, et al [16,17] further analyzed their efficiency.…”

Section: Introductionmentioning

confidence: 99%

Watermarking PDF Documents using Various Representations of Self-inverting Permutations

Chroni

Nikolopoulos

2015

Proceedings of the 11th International Conference on Web Information Systems and Technologies

View full text Add to dashboard Cite

This work provides to web users copyright protection of their Portable Document Format (PDF) documents by proposing efficient and easily implementable techniques for PDF watermarking; our techniques are based on the ideas of our recently proposed watermarking techniques for software, image, and audio, expanding thus the digital objects that can be efficiently watermarked through the use of self-inverting permutations. In particular, we present various representations of a selfinverting permutation π * namely 1D-representation, 2D-representation, and RPG-representation, and show that theses representations can be efficiently applied to PDF watermarking. Indeed, we first present an audio-based technique for marking a PDF document T by exploiting the 1D-representation of a permutation π * , and then, since pages of a PDF document T are 2D objects, we present an image-based algorithm for encoding π * into T by first mapping the elements of π * into a matrix A * and then using the information stored in A * to mark invisibly specific areas of PDF document T . Finally, we describe a graph-based watermarking algorithm for embedding a self-inverting permutation π * into the document structure of a PDF file T by exploiting the RPGrepresentation of π * and the structure of a PDF document. We have evaluated the embedding and extracting algorithms by testing them on various and different in characteristics PDF documents.

show abstract

Electronic marking and identification techniques to discourage document copying

Cited by 284 publications

References 14 publications

Arabic Text Watermarking : A Review

Arabic Text Watermarking : A Review

Damageless Watermark Extraction Using Nonlinear Feature Extraction Scheme Trained on Frequency Domain

Watermarking PDF Documents using Various Representations of Self-inverting Permutations

Contact Info

Product

Resources

About