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A high-capacity, data-hiding algorithm that lets the user restore the original host image after retrieving the hidden data is presented in this paper. The proposed algorithm can be used for watermarking valuable or sensitive images such as original art works or military and medical images. The proposed algorithm is based on a generalized, reversible, integer transform, which calculates the average and pair-wise differences between the elements of a vector extracted from the pixels of the image. The watermark is embedded into a set of carefully selected coefficients by replacing the least significant bit (LSB) of every selected coefficient by a watermark bit. Most of these coefficients are shifted left by one bit before replacing their LSBs. Several conditions are derived and used in selecting the appropriate coefficients to ensure that they remain identifiable after embedding. In addition, the selection of coefficients ensures that the embedding process does not cause any overflow or underflow when the inverse of the transform is computed. To ensure reversibility, the locations of the shifted coefficients and the original LSBs are embedded in the selected coefficients before embedding the desired payload. Simulation results of the algorithm and its performance are also presented and discussed in the paper.

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Many articles covering novel techniques, theoretical studies, attacks, and analyses have been published recently in the field of digital watermarking. In the interest of expanding commercial markets and applications of watermarking, this paper is part of a series of papers from Digimarc on practical issues associated with commercial watermarking applications. In this paper we address several practical issues associated with the use of web cameras for watermark detection. In addition to the obvious issues of resolution and sensitivity, we explore issues related to the tradeoff between gain and integration time to improve sensitivity, and the effects of fixed pattern noise, time variant noise, and lens and Bayer pattern distortions. Furthermore, the ability to control (or at least determine) camera characteristics including white balance, interpolation, and gain have proven to be critical to successful application of watermark readers based on web cameras. These issues and tradeoffs are examined with respect to typical spatial-domain and transform-domain watermarking approaches.

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