基于量化的水印嵌入算法可以实现盲检测,QIM(Quantized Index Modulation)是最常见的量化嵌入方法。量化步长是影响量化水印算法性能的最重要因素之一。本论文基于视觉模型的特点,针对多种具体的攻击,提出了对视觉模型进一步改进以及改进视觉模型下的四种不同水印嵌入算法,并将其与QIM相结合。实验结果表明本论文提出的算法对噪声干扰和常见的图像处理均具有较好的鲁棒性。论文最后给出总结和展望。
In this paper, three robust zero-watermark algorithms named Direct Current coefficient RElationship (DC-RE), CUmulant combined Singular Value Decomposition (CU-SVD), and CUmulant combined Singular Value Decomposition RElationship (CU-SVD-RE) are proposed. The algorithm DC-RE gets the feature vector from the relationship of DC coefficients between adjacent blocks, CU-SVD gets the feature vector from the singular value of third-order cumulants, while CU-SVD-RE combines the essence of the first two algorithms. Specially, CU-SVD-RE gets the feature vector from the relationship between singular values of third-order cumulants. Being a cross-over studying field of watermarking and cryptography, the zero-watermark algorithms are robust without modifying the carrier. Numerical simulation obviously shows that, under geometric attacks, the performance of CU-SVD-RE and DC-RE algorithm are better and all three proposed algorithms are robust to various attacks, such as median filter, salt and pepper noise, and Gaussian low-pass filter attacks.
