Digital watermarking, information embedding, and data hiding systems embed information, sometimes called a digital watermark, inside a host signal, which is typically an image, audio signal, or video signal. The host signal is not degraded unacceptably in the process, and one can recover the watermark even if the composite host and watermark signal undergo a variety of corruptions and attacks as long as these corruptions do not unacceptably degrade the host signal.
These systems play an important role in meeting at least three major challenges that result from the widespread use of digital communication networks to disseminate multimedia content: (1) the relative ease with which one can generate perfect copies of digital signals creates a need for copyright protection mechanisms, (2) the relative ease with which one can alter digital signals creates a need for authentication and tamper-detection methods, and (3) the increase in sheer volume of transmitted data creates a demand for bandwidth-efficient methods to either backwards-compatibly increase capacities of existing legacy networks or deploy new networks backwards-compatibly with legacy networks.
We introduce a framework within which to design and analyze digital watermarking and information embedding systems. In this framework performance is characterized by achievable rate-distortion-robustness trade-offs, and this framework leads quite naturally to a new class of embedding methods called quantization index modulation (QIM). These QIM methods, especially when combined with postprocessing called distortion compensation, achieve provably better rate-distortion-robustness performance than previously proposed classes of methods such as spread spectrum methods and generalized low-bit modulation methods in a number of different scenarios, which include both intentional and unintentional attacks. Indeed, we show that distortion-compensated QIM methods can achieve capacity, the information-theoretically best possible rate-distortion-robustness performance, against both additive Gaussian noise attacks and arbitrary squared error distortion-constrained attacks. These results also have implications for the problem of communicating over broadcast channels. We also present practical implementations of QIM methods called dither modulation and demonstrate their performance both analytically and through empirical simulations. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)
Cited By
- Kotagiri S and Laneman J (2010). Variations on information embedding in multiple access and broadcast channels, IEEE Transactions on Information Theory, 56:5, (2225-2240), Online publication date: 1-May-2010.
- Lu W, Li W, Safavi-Naini R and Ogunbona P Optimal image watermark decoding Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing, (141-149)
- Cox I and Miller M (2002). The first 50 years of electronic watermarking, EURASIP Journal on Advances in Signal Processing, 2002:2, (126-132), Online publication date: 1-Feb-2002.
- Cox I and Miller M (2002). The first 50 years of electronic watermarking, EURASIP Journal on Advances in Signal Processing, 2002:1, (126-132), Online publication date: 1-Jan-2002.
- Chen B and Wornell G (2001). Quantization Index Modulation Methods for Digital Watermarking and Information Embedding of Multimedia, Journal of VLSI Signal Processing Systems, 27:1-2, (7-33), Online publication date: 1-Feb-2001.
Recommendations
Lossless data embedding--new paradigm in digital watermarking
Emerging applications of multimedia data hidingOne common drawback of virtually all current data embedding methods is the fact that the original image is inevitably distorted due to data embedding itself. This distortion typically cannot be removed completely due to quantization, bit-replacement, or ...
Hiding Information Employing Reduplicating Embedding
APSCC '08: Proceedings of the 2008 IEEE Asia-Pacific Services Computing ConferenceIn this paper, a novel data hiding method employing reduplicating embedding is proposed. This method uses seven most significant bits to embed two secret digits and sets the least significant bit as an indicator. Therefore, two secret digits can be ...