Paper
1 August 2001 Data hiding capacity in the presence of an imperfectly known channel
Author Affiliations +
Proceedings Volume 4314, Security and Watermarking of Multimedia Contents III; (2001) https://doi.org/10.1117/12.435435
Event: Photonics West 2001 - Electronic Imaging, 2001, San Jose, CA, United States
Abstract
We consider a data hiding channel in this paper that is not perfectly known by the encoder and the decoder. The imperfect knowledge could be due to the channel estimation error, time-varying active adversary etc. A mathematical model for this scenario is proposed. Many important attacks such as scaling, geometrical transformations etc. fall under the proposed mathematical model. Minimal assumptions are made regarding the probability distributions of the data-hiding channel. Lower and upper bounds on the data hiding capacity are derived. It is shown that the popular additive Gaussian noise channel model may not suffice in real-world scenarios; the capacity estimates using the additive Gaussian channel model tend to either over- or under-estimate the capacity under different scenarios. Asymptotic value of the capacity as the signal to noise ratio becomes arbitrarily large is also given. Many existing data hiding capacity estimates are observed to be a special case of the formulas derived in this paper. We also observe that the proposed mathematical model can be applied to real-life applications such as data hiding in image/video. Theoretical results are further explained using numerical values.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Rajarathnam Chandramouli "Data hiding capacity in the presence of an imperfectly known channel", Proc. SPIE 4314, Security and Watermarking of Multimedia Contents III, (1 August 2001); https://doi.org/10.1117/12.435435
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Cited by 11 scholarly publications.
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KEYWORDS
Data hiding

Mathematical modeling

Error analysis

Signal to noise ratio

Receivers

Digital watermarking

Data modeling

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