Download Data Hiding - Fundamentals and Applications by Husrev T. Sencar PDF

By Husrev T. Sencar

Multimedia applied sciences have gotten extra refined, permitting the net to house a speedily starting to be viewers with a whole diversity of companies and effective supply equipment. even if the web now places conversation, schooling, trade and socialization at our finger suggestions, its speedy progress has raised a few weighty protection issues with appreciate to multimedia content material. The proprietors of this content material face huge, immense demanding situations in safeguarding their highbrow estate, whereas nonetheless exploiting the net as a big source for commerce.
Data Hiding basics and functions specializes in the idea and state of the art functions of content material safety and information hiding in electronic multimedia. one of many pillars of content material safety options is the imperceptible insertion of knowledge into multimedia information for safety reasons; the assumption is this inserted info will permit detection of unauthorized usage.
* offers a theoretical framework for info hiding, in a sign processing context;
* sensible functions in safe, multimedia delivery;
* Compression strong information hiding;
* info hiding for facts of ownership--WATERMARKING;
* information hiding algorithms for snapshot and video watermarking.

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Extra info for Data Hiding - Fundamentals and Applications

Example text

WM , for the given host signal C. In type II and type III methods, each message or watermark sample is assigned a particular quantizer Q (·). The base quantizer Q (·) may be a high dimensional vector quantizer or a Cartesian product of scalar quantizers with as the distance between the reconstruction points. For type II embedding, C is quantized with respect into the watermark signal, Q (C, W). Consequently, the codeword X is the quantization error introduced to the host signal C, X = Q (C, W) − C.

Hernandez et al. [58] proposed a more thorough model, which analyzes the performance of a proposed data hiding method. In this model, L orthogonal sequences are used for the signature. The image is decomposed into channels corresponding to its projections onto each of the orthogonal signatures. The capacity of the channels is calculated for unprocessed images and for images that have gone through filtering operations. 2 Problem Statement Let I be the original (cover) image, to which a message W (a representation for embedded information bits) is added, such that Iˆ = I+W.

Each bin is associated with the index of a message and points to 2N(I(U,C)+ ) sequences. This collection of sequences is made known to both encoder and decoder. In order to generate the codeword, the side information C is weighted by the proper α and subtracted from the sequences in the bin corresponding to the message to be conveyed. Among the resulting signals, the one that is orthogonal to C (|(Uj − α ∗ C)T C| < δ, j = 1, . . , 2N(I(U,C)+ ) , for a proper δ value) and also satisfies the power constraint ( N1 X 2 ≤ P) is the optimal codeword corresponding to the message index being sent.

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