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An adaptive QIM- and SVD-based digital image watermarking scheme in the wavelet domain

机译:基于自适应的基于Qim和SVD的数字图像水印方案在小波域中

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This paper presents a blind, adaptive quantization index modulation (QIM)- and singular value decomposition (SVD)-based watermarking scheme to embed watermark bits in the approximation subband of the wavelet domain. The QIM technique adaptively determines the quantization step for each embedding block using a statistical model. The SVD technique uses the quantization step to modify the SVs of each embedding block. This modification ensures the SVs of highly textured blocks are largely modified and the SVs of smooth textured blocks are slightly modified. The successive packing interleaving (SPI) scheme and the one-way hashing functions are respectively applied to improve the robustness of the proposed system. A statistical-clustering method is also applied offline to decide two optimal weighting parameters for the QIM technique so the adaptive quantization steps are optimal for all embedding blocks. Experimental results demonstrate our scheme is robust against JPEG compressions down to a level of 20% quality factor. It also performs better than a peer SVD-based wavelet domain watermarking approach.
机译:本文呈现了盲,自适应量化指标调制(QIM)和奇异值分解(SVD)的基于水印方案,以嵌入小波域的近似子带中的水印比特。 QIM技术使用统计模型自适应地确定每个嵌入块的量化步骤。 SVD技术使用量化步骤来修改每个嵌入块的SV。此修改可确保高度纹理块的SV在很大程度上修改,平滑纹理块的SV略微修改。连续的包装交织(SPI)方案和单向散列函数分别应用于提高所提出的系统的鲁棒性。统计聚类方法还将脱机应用,以确定QIM技术的两个最佳加权参数,因此自适应量化步骤对于所有嵌入块是最佳的。实验结果表明,我们的计划对JPEG压缩的强大稳健至低于20%的质量因素。它还比基于对等SVD的小波域水印方法更好。

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