Watermarking PDF Documents using Various Representations of Self-inverting Permutations

TL;DR

This paper introduces novel PDF watermarking techniques utilizing self-inverting permutations in 1D, 2D, and RPG representations, enabling efficient, invisible, and structurally embedded copyright protection for PDF documents.

Contribution

It extends self-inverting permutation-based watermarking methods to PDF documents through three distinct representations and algorithms for audio, image, and structural embedding.

Findings

Effective watermark embedding and extraction demonstrated on various PDFs.

Invisibility and robustness of watermarks confirmed through testing.

Multiple representations allow versatile watermarking approaches.

Abstract

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 self-inverting permutation $\pi^*$ 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 $\pi^*$, and then, since pages of a PDF document $T$ are 2D objects, we present an image-based algorithm for encoding $\pi^*$ into $T$ by first mapping the elements of $\pi^*$ 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 $\pi^*$ into the document structure of a PDF file $T$ by exploiting the RPG-representation of $\pi^*$ 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.