Quantization-based data hiding robust to linear-time-invariant filtering
Quantization-based data hiding robust to linear-time-invariant filtering
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DOI：
10.1109/tifs.2008.922057
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发表时间：
2008-06-01
影响因子：
6.8
通讯作者：
Mosquera, Carlos
中科院分区：
文献类型：
--
作者：
Perez-Gonzalez, Fernando;Mosquera, Carlos
基于量化的方法，如抖动调制(DM)，由于其宿主抑制能力而获得了广泛的接受，在加性白高斯信道中提供了比基于扩频的方法显著的性能改进。遗憾的是，现有的基于量化的方案不能抵抗简单的线性时不变(LTI)滤波，这是多媒体信号的常见操作。我们提出了一种新的算法，称为离散傅里叶变换有理抖动调制(DFT-RDM)，该算法对LTI滤波具有较强的鲁棒性，并且不需要在嵌入器或检测器处假设任何滤波器的先验知识。DFT-RDM基本上将DFT运算与对幅度缩放稳健的基于量化的方案相结合。在基本DFT-RDM的基础上，提出了两个易于实现的改进：加窗和扩展。特别是，后者导致的性能收益远远大于在常规DM中传播所获得的收益。对于基本的DFT-RDM及其与扩展和加窗的组合，我们还对我们的方案进行了深入的分析，从而得到了每DFT通道误码率的准确预测和界。这些工具允许设计者选择主要嵌入参数，而不需要实际进行任何模拟。文中还给出了几个实际滤波器的仿真结果，验证了我们的分析。举例说明了DFT-RDM与加窗、扩频和里德-所罗门信道编码相结合的优点。
Quantization-based methods, such as dither modulation (DM), have gained wide acceptance due to their host rejection capabilities Which afford significant performance gains over spread-spectrum-based methods in additive white Gaussian channels. Unfortunately, existing quantization-based schemes are not robust against simple linear-time-invariant (LTI) filtering, which is a common operation with multimedia signals. We propose a new algorithm, named discrete Fourier transform-rational dither modulation (DFT-RDM) which is robust against LTI filtering and yet does not assume any prior knowledge of the filter at either the embedder or the detector. DFT-RDM basically combines a DFT operation with a quantization-based scheme robust to amplitude scaling. Two easily implementable improvements over the basic DFT-RDM are proposed: windowing and spreading. In particular, the latter leads to performance gains that are much larger than those achieved with spreading in regular DM. We also provide a thorough analysis of our scheme which leads to both accurate predictions and bounds on the per-DFT-channel bit-error rate, for the basic DFT-RDM and its combination with spreading and windowing. These tools let the designer choose the main embedding parameters without actually requiring any simulation. The results of several simulations for practical filters validating our analysis are presented as well. The benefits of combining DFT-RDM with windowing, spreading, and Reed-Solomon channel coding are illustrated with an example.