Digital watermarking

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A digital watermark is a kind of marker covertly embedded in a noise-tolerant signal such as audio, video or image data. [1] It is typically used to identify ownership of the copyright of such signal. "Watermarking" is the process of hiding digital information in a carrier signal; the hidden information should, [2] but does not need to, contain a relation to the carrier signal. Digital watermarks may be used to verify the authenticity or integrity of the carrier signal or to show the identity of its owners. It is prominently used for tracing copyright infringements and for banknote authentication.

Contents

Like traditional physical watermarks, digital watermarks are often only perceptible under certain conditions, e.g. after using some algorithm. [3] If a digital watermark distorts the carrier signal in a way that it becomes easily perceivable, it may be considered less effective depending on its purpose. [3] Traditional watermarks may be applied to visible media (like images or video), whereas in digital watermarking, the signal may be audio, pictures, video, texts or 3D models. A signal may carry several different watermarks at the same time. Unlike metadata that is added to the carrier signal, a digital watermark does not change the size of the carrier signal.

The needed properties of a digital watermark depend on the use case in which it is applied. For marking media files with copyright information, a digital watermark has to be rather robust against modifications that can be applied to the carrier signal. Instead, if integrity has to be ensured, a fragile watermark would be applied.

Both steganography and digital watermarking employ steganographic techniques to embed data covertly in noisy signals. While steganography aims for imperceptibility to human senses, digital watermarking tries to control the robustness as top priority.

Since a digital copy of data is the same as the original, digital watermarking is a passive protection tool. It just marks data, but does not degrade it or control access to the data.

One application of digital watermarking is source tracking. A watermark is embedded into a digital signal at each point of distribution. If a copy of the work is found later, then the watermark may be retrieved from the copy and the source of the distribution is known. This technique reportedly has been used to detect the source of illegally copied movies.

History

The term "Digital Watermark" was coined by Andrew Tirkel and Charles Osborne in December 1992. The first successful embedding and extraction of a steganographic spread spectrum watermark was demonstrated in 1993 by Andrew Tirkel, Gerard Rankin, Ron Van Schyndel, Charles Osborne, and others. [4]

Watermarks are identification marks produced during the paper making process. The first watermarks appeared in Italy during the 13th century, but their use rapidly spread across Europe. They were used as a means to identify the paper maker or the trade guild that manufactured the paper. The marks often were created by a wire sewn onto the paper mold. Watermarks continue to be used today as manufacturer's marks and to prevent forgery.

Applications

Digital watermarking may be used for a wide range of applications, such as:

Digital watermarking life-cycle phases

General digital watermark life-cycle phases with embedding-, attacking-, and detection and retrieval functions Watermark life cycle.svg
General digital watermark life-cycle phases with embedding-, attacking-, and detection and retrieval functions

The information to be embedded in a signal is called a digital watermark, although in some contexts the phrase digital watermark means the difference between the watermarked signal and the cover signal. The signal where the watermark is to be embedded is called the host signal. A watermarking system is usually divided into three distinct steps, embedding, attack, and detection. In embedding, an algorithm accepts the host and the data to be embedded, and produces a watermarked signal.

Then the watermarked digital signal is transmitted or stored, usually transmitted to another person. If this person makes a modification, this is called an attack. While the modification may not be malicious, the term attack arises from copyright protection application, where third parties may attempt to remove the digital watermark through modification. There are many possible modifications, for example, lossy compression of the data (in which resolution is diminished), cropping an image or video, or intentionally adding noise.

Detection (often called extraction) is an algorithm which is applied to the attacked signal to attempt to extract the watermark from it. If the signal was unmodified during transmission, then the watermark still is present and it may be extracted. In robust digital watermarking applications, the extraction algorithm should be able to produce the watermark correctly, even if the modifications were strong. In fragile digital watermarking, the extraction algorithm should fail if any change is made to the signal.

Classification

A digital watermark is called robust with respect to transformations if the embedded information may be detected reliably from the marked signal, even if degraded by any number of transformations. Typical image degradations are JPEG compression, rotation, cropping, additive noise, and quantization. [6] For video content, temporal modifications and MPEG compression often are added to this list. A digital watermark is called imperceptible if the watermarked content is perceptually equivalent to the original, unwatermarked content. [7] In general, it is easy to create either robust watermarks or imperceptible watermarks, but the creation of both robust and imperceptible watermarks has proven to be quite challenging. [2] Robust imperceptible watermarks have been proposed as a tool for the protection of digital content, for example as an embedded no-copy-allowed flag in professional video content. [8]

Digital watermarking techniques may be classified in several ways.

Robustness

A digital watermark is called "fragile" if it fails to be detectable after the slightest modification. Fragile watermarks are commonly used for tamper detection (integrity proof). Modifications to an original work that clearly are noticeable, commonly are not referred to as watermarks, but as generalized barcodes.

A digital watermark is called semi-fragile if it resists benign transformations, but fails detection after malignant transformations. Semi-fragile watermarks commonly are used to detect malignant transformations.

A digital watermark is called robust if it resists a designated class of transformations. Robust watermarks may be used in copy protection applications to carry copy and no access control information.

Perceptibility

A digital watermark is called imperceptible if the original cover signal and the marked signal are perceptually indistinguishable.

A digital watermark is called perceptible if its presence in the marked signal is noticeable (e.g. digital on-screen graphics like a network logo, content bug, codes, opaque images). On videos and images, some are made transparent/translucent for convenience for consumers due to the fact that they block portion of the view; therefore degrading it.

This should not be confused with perceptual, that is, watermarking which uses the limitations of human perception to be imperceptible.

Capacity

The length of the embedded message determines two different main classes of digital watermarking schemes:

Embedding method

A digital watermarking method is referred to as spread-spectrum if the marked signal is obtained by an additive modification. Spread-spectrum watermarks are known to be modestly robust, but also to have a low information capacity due to host interference.

A digital watermarking method is said to be of quantization type if the marked signal is obtained by quantization. Quantization watermarks suffer from low robustness, but have a high information capacity due to rejection of host interference.

A digital watermarking method is referred to as amplitude modulation if the marked signal is embedded by additive modification which is similar to spread spectrum method, but is particularly embedded in the spatial domain.

Evaluation and benchmarking

The evaluation of digital watermarking schemes may provide detailed information for a watermark designer or for end-users, therefore, different evaluation strategies exist. Often used by a watermark designer is the evaluation of single properties to show, for example, an improvement. Mostly, end-users are not interested in detailed information. They want to know if a given digital watermarking algorithm may be used for their application scenario, and if so, which parameter sets seems to be the best.

Cameras

Epson and Kodak have produced cameras with security features such as the Epson PhotoPC 3000Z and the Kodak DC-290. Both cameras added irremovable features to the pictures which distorted the original image, making them unacceptable for some applications such as forensic evidence in court. According to Blythe and Fridrich, "[n]either camera can provide an undisputable proof of the image origin or its author". [9] A secure digital camera (SDC) was proposed by Saraju Mohanty, et al. in 2003 and published in January 2004. This was not the first time this was proposed. [10] Blythe and Fridrich also have worked on SDC in 2004 [9] for a digital camera that would use lossless watermarking to embed a biometric identifier together with a cryptographic hash. [11]

Reversible data hiding

Reversible data hiding is a technique which enables images to be authenticated and then restored to their original form by removing the digital watermark and replacing the image data that had been overwritten. This would make the images acceptable for legal purposes. The US Army also is interested in this technique for authentication of reconnaissance images. [12] [13]

Watermarking for relational databases

Digital watermarking for relational databases has emerged as a candidate solution to provide copyright protection, tamper detection, traitor tracing, and maintaining integrity of relational data. Many watermarking techniques have been proposed in the literature to address these purposes. A survey of the current state-of-the-art and a classification of the different techniques according to their intent, the way they express the watermark, the cover type, granularity level, and verifiability was published in 2010 by Halder et al. in the Journal of Universal Computer Science. [14]

See also

Related Research Articles

<span class="mw-page-title-main">Computer vision</span> Computerized information extraction from images

Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions. Understanding in this context means the transformation of visual images into descriptions of the world that make sense to thought processes and can elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory.

Digital signal processing (DSP) is the use of digital processing, such as by computers or more specialized digital signal processors, to perform a wide variety of signal processing operations. The digital signals processed in this manner are a sequence of numbers that represent samples of a continuous variable in a domain such as time, space, or frequency. In digital electronics, a digital signal is represented as a pulse train, which is typically generated by the switching of a transistor.

<span class="mw-page-title-main">Lossy compression</span> Data compression approach that reduces data size while discarding or changing some of it

In information technology, lossy compression or irreversible compression is the class of data compression methods that uses inexact approximations and partial data discarding to represent the content. These techniques are used to reduce data size for storing, handling, and transmitting content. The different versions of the photo of the cat on this page show how higher degrees of approximation create coarser images as more details are removed. This is opposed to lossless data compression which does not degrade the data. The amount of data reduction possible using lossy compression is much higher than using lossless techniques.

Speech coding is an application of data compression to digital audio signals containing speech. Speech coding uses speech-specific parameter estimation using audio signal processing techniques to model the speech signal, combined with generic data compression algorithms to represent the resulting modeled parameters in a compact bitstream.

Steganography is the practice of representing information within another message or physical object, in such a manner that the presence of the information is not evident to human inspection. In computing/electronic contexts, a computer file, message, image, or video is concealed within another file, message, image, or video. The word steganography comes from Greek steganographia, which combines the words steganós, meaning "covered or concealed", and -graphia meaning "writing".

Transform coding is a type of data compression for "natural" data like audio signals or photographic images. The transformation is typically lossless on its own but is used to enable better quantization, which then results in a lower quality copy of the original input.

A discrete cosine transform (DCT) expresses a finite sequence of data points in terms of a sum of cosine functions oscillating at different frequencies. The DCT, first proposed by Nasir Ahmed in 1972, is a widely used transformation technique in signal processing and data compression. It is used in most digital media, including digital images, digital video, digital audio, digital television, digital radio, and speech coding. DCTs are also important to numerous other applications in science and engineering, such as digital signal processing, telecommunication devices, reducing network bandwidth usage, and spectral methods for the numerical solution of partial differential equations.

Steganalysis is the study of detecting messages hidden using steganography; this is analogous to cryptanalysis applied to cryptography.

A watermark stored in a data file refers to a method for ensuring data integrity which combines aspects of data hashing and digital watermarking. Both are useful for tamper detection, though each has its own advantages and disadvantages.

Video fingerprinting or video hashing are a class of dimension reduction techniques in which a system identifies, extracts, and then summarizes characteristic

Ali Naci Akansu is a Turkish-American Professor of electrical & computer engineering and scientist in applied mathematics.

An audio watermark is a unique electronic identifier embedded in an audio signal, typically used to identify ownership of copyright. It is similar to a watermark on a photograph.

<span class="mw-page-title-main">Cinavia</span> Analog watermarking and steganography system

Cinavia, originally called Verance Copy Management System for Audiovisual Content (VCMS/AV), is an analog watermarking and steganography system under development by Verance since 1999, and released in 2010. In conjunction with the existing Advanced Access Content System (AACS) digital rights management (DRM) inclusion of Cinavia watermarking detection support became mandatory for all consumer Blu-ray Disc players from 2012.

<span class="mw-page-title-main">Steganography tools</span> Software for embedding hidden data inside a carrier file

A steganography software tool allows a user to embed hidden data inside a carrier file, such as an image or video, and later extract that data.

Video copy detection is the process of detecting illegally copied videos by analyzing them and comparing them to original content.

<span class="mw-page-title-main">OpenPuff</span>

OpenPuff Steganography and Watermarking, sometimes abbreviated OpenPuff or Puff, is a free steganography tool for Microsoft Windows created by Cosimo Oliboni and still maintained as independent software. The program is notable for being the first steganography tool that:

BPCS-steganography is a type of digital steganography.

Robust Principal Component Analysis (RPCA) is a modification of the widely used statistical procedure of principal component analysis (PCA) which works well with respect to grossly corrupted observations. A number of different approaches exist for Robust PCA, including an idealized version of Robust PCA, which aims to recover a low-rank matrix L0 from highly corrupted measurements M = L0 +S0. This decomposition in low-rank and sparse matrices can be achieved by techniques such as Principal Component Pursuit method (PCP), Stable PCP, Quantized PCP, Block based PCP, and Local PCP. Then, optimization methods are used such as the Augmented Lagrange Multiplier Method (ALM), Alternating Direction Method (ADM), Fast Alternating Minimization (FAM), Iteratively Reweighted Least Squares (IRLS ) or alternating projections (AP).

ZPEG is a motion video technology that applies a human visual acuity model to a decorrelated transform-domain space, thereby optimally reducing the redundancies in motion video by removing the subjectively imperceptible. This technology is applicable to a wide range of video processing problems such as video optimization, real-time motion video compression, subjective quality monitoring, and format conversion.

A copy detection pattern (CDP) or graphical code is a small random or pseudo-random digital image which is printed on documents, labels or products for counterfeit detection. Authentication is made by scanning the printed CDP using an image scanner or mobile phone camera. It is possible to store additional product-specific data into the CDP that will be decoded during the scanning process. A CDP can also be inserted into a 2D barcode to facilitate smartphone authentication and to connect with traceability data.

References

  1. H.T. Sencar, M. Ramkumar and A.N. Akansu: Data Hiding Fundamentals and Applications: Content Security in Digital Multimedia. Academic Press, San Diego, CA, USA, 2004.
  2. 1 2 Ingemar J. Cox: Digital watermarking and steganography. Morgan Kaufmann, Burlington, MA, USA, 2008
  3. 1 2 Frank Y. Shih: Digital watermarking and steganography: fundamentals and techniques. Taylor & Francis, Boca Raton, FL, USA, 2008
  4. A.Z.Tirkel, G.A. Rankin, R.M. Van Schyndel, W.J.Ho, N.R.A.Mee, C.F.Osborne. “Electronic Water Mark”. DICTA 93, Macquarie University. p.666-673
  5. Zigomitros, Athanasios; Papageorgiou, Achilleas; Patsakis, Constantinos (2012). "Social network content management through watermarking". 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications. IEEE. pp. 1381–1386. doi:10.1109/TrustCom.2012.264. ISBN   978-1-4673-2172-3. S2CID   17845019.
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  9. 1 2 BLYTHE, Paul; FRIDRICH, Jessica (August 2004). "Secure digital camera" (PDF). Digital Forensic Research Workshop: 11–13. Archived (PDF) from the original on 2010-06-10. Retrieved 23 July 2018.
  10. Mohanty, Saraju P.; Ranganathan, Nagarajan; Namballa, Ravi K. (2004). "VLSI implementation of visible watermarking for secure digital still camera design" (PDF). 17th International Conference on VLSI Design. Proceedings. IEEE. pp. 1063–1068. doi:10.1109/ICVD.2004.1261070. ISBN   0-7695-2072-3. S2CID   1821349. Archived from the original (PDF) on 4 March 2016.
  11. Toshikazu Wada; Fay Huang (2009), Advances in Image and Video Technology, Lecture Notes in Computer Science, vol. 5414, pp. 340–341, Bibcode:2008LNCS.5414.....W, doi:10.1007/978-3-540-92957-4, ISBN   978-3-540-92956-7
  12. Unretouched by human hand, The Economist, December 12, 2002
  13. "Unretouched by human hand". Technology Quarterly. The Economist. December 12, 2002. Archived from the original on 2009-08-04. Retrieved 4 August 2009.
  14. Halder, Raju; Pal, Shantanu; Cortesi, Agostino (2010). "Watermarking Techniques for Relational Databases: Survey, Classification and Comparison". Journal of Universal Computer Science. 16 (21): 3164–3190. CiteSeerX   10.1.1.368.1075 .

Further reading