Image-Adaptive Spread Transform Dither Modulation Using Human Visual Model.
ABSTRACT This paper presents a new approach on image-adaptive spread-transform dither modulation (STDM). The approach is performed in the discrete cosine transform (DCT) domain, and modifies the original STDM in such a way that the spread vector is weighted by a set of just noticeable differences (JND's) derived from Watson's model before it is added to the cover work. An adaptive quantization step size is next determined according to the following two constraints: 1) the covered work is perceptually acceptable, which is measured by a global perceptual distance; 2) the covered work is within the detection region. We derive the strategy on the choice of the quantization step. Further, an effective solution is proposed to deal with the amplitude scaling attack, where the scaled quantization step is produced using an extracted signal in proportion to the amplitudes of the cover work. Experimental results demonstrate that the proposed approach achieves the improved robustness and fidelity
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ABSTRACT: We consider the problem of embedding one signal (e.g., a digital watermark), within another “host” signal to form a third, “composite” signal. The embedding is designed to achieve efficient tradeoffs among the three conflicting goals of maximizing the information-embedding rate, minimizing the distortion between the host signal and composite signal, and maximizing the robustness of the embedding. We introduce new classes of embedding methods, termed quantization index modulation (QIM) and distortion-compensated QIM (DC-QIM), and develop convenient realizations in the form of what we refer to as dither modulation. Using deterministic models to evaluate digital watermarking methods, we show that QIM is “provably good” against arbitrary bounded and fully informed attacks, which arise in several copyright applications, and in particular it achieves provably better rate distortion-robustness tradeoffs than currently popular spread-spectrum and low-bit(s) modulation methods. Furthermore, we show that for some important classes of probabilistic models, DC-QIM is optimal (capacity-achieving) and regular QIM is near-optimal. These include both additive white Gaussian noise (AWGN) channels, which may be good models for hybrid transmission applications such as digital audio broadcasting, and mean-square-error-constrained attack channels that model private-key watermarking applicationsIEEE Transactions on Information Theory 06/2001; · 2.62 Impact Factor
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ABSTRACT: This paper proposes a new oblivious imageadaptive watermarking technique which utilizes a visual model in the discrete cosine transform (DCT) domain and is based on quantization index modulation. In this scheme, the image is watermarked by modifying selected DCT coefficients of image blocks under a constraint specified by the visual model. The binary watermark is recovered from the watermarked image without any knowledge of the original image. The robustness of the algorithm to various attacks is evaluated and presented.03/2002;
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ABSTRACT: In this letter, we propose a novel, yet simple, image-adaptive watermarking scheme for image authentication by applying a simple quantization-index-modulation process on wavelet domain singular value decomposition. Unlike the traditional wavelet-based watermarking schemes where the watermark bits are embedded directly on the wavelet coefficients, the proposed scheme is based on bit embedding on the singular value (luminance) of the blocks within wavelet subband of the original image. To improve the fidelity and the perceptual quality of the watermarked image and to enhance the security of watermarking, we model the adaptive quantization parameters based on the statistics of blocks within subbands. The scheme is robust against JPEG compression but extremely sensitive to malicious manipulation such as filtering and random noising. Watermark detection is efficient and blind in the sense only the quantization parameters but not the original image are required. The quantization parameters adaptive to blocks are vector quantized to reduce the watermarking overhead.IEEE Transactions on Circuits and Systems for Video Technology 02/2005; · 1.82 Impact Factor