By Husrev T. Sencar
Multimedia applied sciences have gotten extra subtle, allowing the web to deal with a speedily growing to be viewers with an entire variety of providers and effective supply equipment. even though the net now places communique, schooling, trade and socialization at our finger tips, its swift development has raised a few weighty safeguard issues with recognize to multimedia content material. The vendors of this content material face huge, immense demanding situations in safeguarding their highbrow estate, whereas nonetheless exploiting the web as a huge source for trade. facts Hiding basics and functions specializes in the speculation and state of the art purposes of content material safeguard and knowledge hiding in electronic multimedia. one of many pillars of content material defense recommendations is the imperceptible insertion of data into multimedia facts for safeguard reasons; the assumption is this inserted details will enable detection of unauthorized utilization. * offers a theoretical framework for info hiding, in a sign processing context; * practical purposes in safe, multimedia supply; * Compression powerful facts hiding; * information hiding for evidence of ownership--WATERMARKING; * facts hiding algorithms for photograph and video watermarking.
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Extra resources for Data Hiding Fundamentals and Applications: Content Security in Digital Multimedia
4 A Review of Data Hiding Methods 23 additive schemes. In this method, a watermark signal is selected such that when the linear correlation between the watermark signal and the undistorted stego signal is quantized, the resulting signal is a centroid of the lattice associated with the embedded signal. The probability of error performance of this method is improved by further processing. Consequently, the watermark signal is selected such that rather than the quantized correlation metric itself, the properly scaled error due to quantization of the correlation metric is mapped to the desired centroid.
Similarly, Chou et al. in , , based on a duality with the distributed source coding problem, implement the exhaustive codeword generation for Costa’s scheme by using a robust optimization method through the use of trellis coded quantizers. In this research direction, the most popular embedding technique is a low complexity implementation of QIM that relies on uniform scalar quantization, which is called dither modulation (DM) . In fact, the earliest data hiding methods , , , , which modified only one or two least significant bits (LSBs) of the host signal, were based on the same principle in rejecting the host signal interference, so-called low bit modulation (LBM).
The number of quantizers in the set corresponds to the number of messages or message letters. Each quantizer of the set is uniquely described by a set of reconstruction points that are nonoverlapping with other sets of reconstruction points. Therefore, each finite state of U is a sequence with values restricted to reconstruction values of the designated quantizers. The terms X and Xt are the embedding distortion due to quantization and the processing distortion, respectively. The codeword corresponding to a message is the distortion signal introduced into the host signal as a result of the embedding operation, S − C.