The digital multimedia, including text, image, graphics, audio, video, and so forth, has become a main way for information communication along with the popularization of Internet and the development of multimedia techniques. How to provide copyright protection has drawn extensive attention in recent years. As a main method for copyright protection, digital watermarking has been widely studied and applied. In this chapter we discuss the important properties of watermarks, the capacity, and the detection error rate. The watermarking system is analyzed based on the channel capacity and error rate of the communication system, and the relation between the detection error rate with the capacity and payload capacity is derived. This chapter also introduces a new analysis method of the watermarking capacity, which is based on the theories of attractors and attraction basin of artificial neural network. The attraction basin of neural network decides the upper limit of watermarking, and the attractors of neural network decide the lower limit of watermarking. According to the experimental results, the detection error rate of watermark is mainly influenced by the watermark average energy and the watermarking capacity. The error rate rises with the increase of watermarking capacity. When the channel coding is used, the watermarking error rate drops with the decrease of the payload capacity of watermarking.
Digital representations of copyrighted material such as movies, songs, and photographs offer many advantages. However, the fact that an unlimited number of perfect copies can be illegally produced is a serious threat to the rights of content owners. Until recently, the primary tool available to help protect content owners’ rights has been encryption. Encryption protects content during the transmission of the data from the sender to receiver. However, after receipt and subsequent decryption, the data is no longer protected and is in the clear. Watermarking compliments encryption. A digital watermark is a piece of information that is hidden directly in media content, in such a way that it is imperceptible to a human observer, but easily detected by a computer. The principal advantage of this is that the content is inseparable from the watermark.
Until the early nineties, digital watermarking techniques had received very much less attention from the research community and from industry than cryptography, but this has changed rapidly since. The first academic conference on the subject was organized in 1996. It was followed by several other conferences focusing on information hiding as well as watermarking.
The main driving force is concern over protecting copyright; as audio, video and other works become available in digital form, it may be that the ease with which perfect copies can be made will lead to large-scale unauthorized copying which will undermine the music, film, book and software publishing industries. There has therefore been significant recent research into watermarking (hidden copyright messages) and fingerprinting (hidden serial numbers or a set of characteristics that tend to distinguish an object from other similar objects); the idea is that the latter can be used to detect copyright violators and the former to prosecute them. But there are many other applications of increasing interest to both the academic and business communities, including anonymous communications, covert channels in computer systems, detection of hidden information, steganography, etc.
Today, cryptographical techniques have reached a level of sophistication such that properly encrypted communications can be assumed secure well beyond the useful life of the information transmitted. In fact, it’s projected that the most powerful algorithms using multi kilobit key lengths could not be comprised through brute force, even if all the computing power worldwide for the next 20 years was focused on the attack. Of course the possibility exists that vulnerabilities could be found, or computing power breakthroughs could occur, but for most users in most applications, current cryptographic techniques are generally sufficient.
Watermarking is very similar to steganography in a number of respects. Both seek to embed information inside a cover message with little to no degradation of the cover-object. Watermarking however adds the additional requirement of robustness. An ideal steganographic system would embed a large amount of information, perfectly securely with no visible degradation to the cover object. An ideal watermarking system however would embed an amount of information that could not be removed or altered without making the cover object entirely unusable. As a side effect of these different requirements, a watermarking system will often trade capacity and perhaps even some security for additional robustness.
A digital watermark embeds an imperceptible signal into data such as audio, video and images, for a variety of purposes, including captioning and copyright control. As watermarking is increasingly used for a wide variety of applications, various properties of watermarks, such as how they respond to common signal transformations or deliberate attack, have become important considerations. In this chapter we discuss the important properties of watermarks, the capacity and the detection error rate.
The watermarking capacity of digital image is the number of bits that can be embedded in a given host image. The performance of watermarking detection is measured by the bit error rate (BER) or probability of error PB. The bit error rate is the number of error bits in the total length of information messages bits. The detection reliability of watermarking closely correlates to two other parameters, which are the watermarking capacity and robustness. The robustness denotes the performance towards intentional and unintentional attacks. The main requirement of robustness is to resist different kind of distortions introduced by common processing and/or malicious attacks while satisfying the imperceptibility criteria.
Key Terms in this Chapter
Attractors: Attractors of Hopfield network represent the stored patterns.
Bit Error Rate (BER): The bit error rate of watermarking detection is the number of error bits in the total length of information messages bits.
Payload Capacity: It is the size (in bits) of the watermark messages actually embedded, associated to a certain decoding error rate.
Theoretical Capacity: It is a theoretical limit on the amount of error-free emendable watermark messages, or inversely, on the minimum probability of error attainable for the given messages.
Hopfield Neural Network: The Hopfield neural network is a recurrent neural network that stores information in a dynamically stable configuration.
Human Vision System (HVS): The Human Vision System (HVS) describes the human vision mechanism such as the spatial frequency orientation, the sensitivity on local contrast and the masking.
Watermarking Capacity: The watermarking capacity of digital image is the number of bits that can be embedded in a given host image.
Digital Watermark: A digital watermark embeds an imperceptible signal into data such as audio, video and images, for a variety of purposes, including captioning and copyright control.
Basin of Attraction: The basin of attraction is the set of states in the system within which almost all states flow to one attractor.
Complete Chapter List
Shiguo Lian, Yan Zhang
Shiguo Lian, Yan Zhang
Pramod A. Jamkhedkar, Gregory L. Heileman
Deepali Brahmbhatt, Mark Stamp
Mercè Serra Joan, Bert Greevenbosch, Anja Becker, Harald Fuchs
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Nicolas Anciaux, Luc Bouganim, Philippe Pucheral
Guojun Wang, Yirong Wu, Geyong Min, Ronghua Shi
Supavadee Aramvith, Rhandley D. Cajote
M. Hassan Shirali-Shahreza, Mohammad Shirali-Shahreza
Pradeep K. Atrey, Abdulmotaleb El Saddik, Mohan Kankanhalli
Esther Palomar, Juan M.E. Tapiador, Julio C. Hernandez-Castro, Arturo Ribagorda
Andreas U. Schmidt, Nicolai Kuntze
Goo-Rak Kwon, Sung-Jea Ko
Frank Y. Shih, Yi-Ta Wu
Guangjie Liu, Shiguo Lian, Yuewei Dai, Zhiquan Wang
Minglei Liu, Ce Zhu
Hsuan T. Chang, Chih-Chung Hsu