A large-capacity zero-watermarking algorithm for color images based on combined transform domain
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摘要:
针对现有大多数彩色图像零水印方案中水印嵌入容量小且抗几何攻击鲁棒性能不佳等问题，提出一种基于组合变换域和位平面分解的大容量鲁棒零水印算法。对彩色载体图像的R、G、B通道分别进行快速有限剪切波变换（FFST），并对变换后的各低频子带进行非重叠分块置乱，将置乱后的各子带的分块离散余弦变换（DCT）系数用于构造四元数离散余弦变换（QDCT）系数矩阵；在每个QDCT系数矩阵中选取两个低频系数，利用其各个实部和虚部的符号极性构造8个二值鲁棒特征矩阵；将经过正交拉丁方置乱加密后的灰度水印图像按位分解，把位分解后的8个二值位平面分别与以上二值鲁棒特征矩阵进行异或并重组得到灰度级认证零水印。此外，在水印检测前，采用ORB算法对待认证图像进行几何校正。实验结果表明，所提算法水印嵌入容量和安全性高，且对于常规非几何攻击和几何攻击均具有较强的鲁棒性。
Abstract:In view of the small watermark embedding capacity and poor robustness against geometric attacks in most of the existing zero-watermarking schemes for color images, a robust zero-watermarking algorithm with a large capacity based on combined transform domain and bit-plane decomposition was proposed. Firstly, the R, G, and B channels of a color carrier image were respectively subjected to fast finite shearlet transform (FFST), and each low-frequency sub-band after FFST was processed by non-overlapping blocking and scrambling. The block discrete cosine transform (DCT) coefficients of each sub-band after the scrambling were used to construct the quaternion discrete cosine transform (QDCT) coefficient matrices, and then two low-frequency coefficients were selected from each QDCT coefficient matrix. Eight binary robust feature matrices were constructed according to the sign polarities of the real parts and the imaginary parts from the above two coefficients. Finally, the gray-level watermark image scrambled and encrypted by the orthogonal Latin squares matrices was decomposed by bits. The eight binary bit-planes after decomposition were used for XOR operation with the above binary robust feature matrices and recombined to obtain the final gray-level authentication zero-watermark. In addition, before the watermark detection, the image to be authenticated was geometrically corrected by the oriented FAST and rotated BRIEF (ORB) algorithm. The experimental results show that the proposed algorithm has high watermark embedding capacity and security, and it is highly robust to both conventional geometric and non-geometric attacks.
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表 1 唯一性测试(NC值)
Table 1. Uniqueness test (NC值)
图像名称 Lena Baboon Peppers Sailboat House Mare Fiore Frog Lena 1.0000 0.5710 0.6210 0.5805 0.5825 0.5498 0.6893 0.5015 Baboon 0.5710 1.0000 0.6011 0.5712 0.6148 0.5173 0.5444 0.5928 Peppers 0.6210 0.6011 1.0000 0.6043 0.6313 0.5322 0.6075 0.5888 Sailboat 0.5805 0.5712 0.6043 1.0000 0.6122 0.5319 0.5773 0.5801 House 0.5825 0.6148 0.6313 0.6122 1.0000 0.5098 0.5192 0.6344 Mare 0.6572 0.5988 0.6529 0.6331 0.6091 1.0000 0.5863 0.5080 Fiore 0.6893 0.5444 0.6075 0.5773 0.5192 0.5863 1.0000 0.4525 Frog 0.5015 0.5928 0.5888 0.5801 0.6344 0.5080 0.4525 1.0000 
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表 2 常规攻击测试结果
Table 2. Test results of conventional attacks
攻击类型及强度 NC值 Lena Baboon Peppers Sailboat House Mare Fiore Frog 高斯噪声(0.1) 0.9810 0.9834 0.9890 0.9762 0.9695 0.9825 0.9747 0.9597 椒盐噪声(0.1) 0.9919 0.9897 0.9953 0.9840 0.9691 0.9907 0.9839 0.9657 中值滤波(5像素×5像素) 0.9982 0.9962 0.9991 0.9955 0.9945 0.9968 0.9961 0.9982 维纳滤波(5像素×5像素) 0.9992 0.9991 0.9998 0.9991 0.9988 0.9992 0.9994 0.9986 JPEG压缩(10) 0.9802 0.9825 0.9927 0.9761 0.9659 0.9846 0.9721 0.9733 JPEG2000压缩(90) 0.9965 0.9927 0.9973 0.9918 0.9836 0.9888 0.9958 0.9855
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表 3 几何攻击测试结果
Table 3. Test results of geometric attacks
攻击类型及强度 NC值 Lena Baboon Peppers Sailboat House Mare Fiore Frog 旋转(10°) 0.9594 0.9877 0.9711 0.9746 0.9724 0.9763 0.9772 0.9566 旋转(90°) 0.9975 0.9992 0.9981 0.9975 0.9991 0.9995 0.9979 0.9943 缩小(0.5) 0.9938 0.9980 0.9988 0.9973 0.9947 0.9958 0.9920 0.9803 放大(2) 0.9960 0.9987 0.9972 0.9965 0.9969 0.9986 0.9951 0.9974 平移(向上10行) 0.9984 0.9967 0.9958 0.9935 0.9955 0.9966 0.9947 0.9882 平移(向左10列) 0.9687 0.9922 0.9897 0.9843 0.9851 0.9910 0.9890 0.9711 剪切(左上角1/16) 0.9722 0.9880 0.9808 0.9830 0.9758 0.9812 0.9808 0.9689 剪切(右上角1/16) 0.9826 0.9883 0.9869 0.9849 0.9797 0.9809 0.9772 0.9622 剪切(中心1/16) 0.9792 0.9846 0.9808 0.9810 0.9836 0.9905 0.9668 0.9728
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表 4 组合攻击类型及参数
Table 4. Types and parameters of combined attacks
序号 攻击类型（参数） 1 高斯噪声（方差0.05）+平移攻击（右移10列） 2 椒盐噪声（强度0.05）+剪切攻击（左上角1/16） 3 维纳滤波（5像素×5像素）+旋转攻击（逆时针10°） 4 中值滤波（7像素×7像素）+JPEG2000压缩（Q=90） 5 JPEG压缩（Q=10）+剪切攻击（中心1/16） 6 剪切攻击（右下角1/16）+平移（上移10行）
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表 6 FFST消融实验
Table 6. Ablation experiment of FFST
分解
层数平均NC值 高斯噪声 椒盐噪声 中值滤波 维纳滤波 JPEG压缩 0.8658 0.8714 0.9616 0.9720 0.9034 1 0.8677 0.8735 0.9654 0.9796 0.9039 2 0.8986 0.9067 0.9856 0.9950 0.9423 3 0.9669 0.9712 0.9961 0.9992 0.9876
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表 7 ORB消融实验
Table 7. Ablation experiment of ORB
是否使用ORB 平均NC值 旋转 缩放 平移 否 0.8512 0.9979 0.9212 是 0.9633 0.9985 0.9855
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表 8 本文算法与其他算法的鲁棒性对比测试结果
Table 8. Comparison of robustness between proposed algorithm and other algorithms
算法 NC值 高斯
噪声(0.05)高斯
噪声(0.1)椒盐
噪声(0.05)椒盐
噪声(0.1)中值滤波
(3像素×
3像素)中值滤波
(5像素×
5像素)维纳滤波
(3像素×
3像素)维纳滤波
(5像素×
5像素)JPEG
压缩(10)JPEG2000
压缩(90)旋转
(10°)旋转
(20°)文献[5] 0.9697 0.9533 0.9833 0.9736 0.9973 0.9946 0.9986 0.9967 0.9945 0.9878 0.7643 0.7059 文献[7] 0.8939 0.8735 0.9168 0.9085 0.9729 0.9580 0.9658 0.9614 0.8918 0.9413 0.8150 0.7992 文献[11] 0.9867 0.9707 0.9974 0.9813 0.9974 0.9974 1.0000 0.9974 0.9921 0.9921 1.0000 1.0000 文献[17] 0.8392 0.8038 0.8944 0.8590 0.9980 0.9834 0.9985 0.9818 0.9085 0.9279 0.8165 0.7794 文献[19] 0.9784 0.9978 0.9515 0.9964 0.9957 0.9970 0.9993 0.9989 0.9890 0.9926 0.6892 0.7520 文献[29] 0.9739 0.9574 0.9802 0.9728 0.9981 0.9949 0.9986 0.9964 0.9743 0.9911 0.8606 0.7616 文献[30] 0.9896 0.9929 1.0000 0.9958 1.0000 1.0000 1.0000 1.0000 0.9963 0.9988 0.9996 0.9996 本文算法 0.9857 0.9810 0.9943 0.9917 0.9990 0.9982 0.9998 0.9992 0.9806 0.9964 0.9595 0.9501 算法 NC值 平均
NC值旋转(40°) 缩小(0.5) 放大(2) 平移
(向上
10行)平移
(向下
10行)平移
(向左
10列)平移
(向右
10列)剪切
(左上角
1/16)剪切
(右上角
1/16)剪切
(左下角
1/16)剪切
(右下角
1/16)剪切
(中心
1/16)文献[5] 0.6855 0.9983 0.9995 0.9277 0.9196 0.8667 0.8845 0.9521 0.9476 0.9554 0.9480 0.9380 0.9309 文献[7] 0.7846 0.9738 0.9795 0.8658 0.8676 0.8631 0.8596 0.9677 0.9576 0.9558 0.9640 0.9560 0.9121 文献[11] 1.0000 1.0000 1.0000 0.8983 0.9153 0.8865 0.8604 0.9687 0.9707 0.9760 0.9761 0.9625 0.9723 文献[17] 0.7689 0.9935 0.9966 0.8450 0.8454 0.8465 0.7924 0.5934 0.7917 0.8606 0.7587 0.9567 0.8711 文献[19] 0.7912 0.9980 0.9992 0.9969 0.9865 0.9692 0.9105 0.8462 0.8338 0.8051 0.7632 0.8723 0.9228 文献[29] 0.5962 0.9989 0.9997 0.9273 0.9242 0.8677 0.8724 0.9523 0.9469 0.9541 0.9465 0.9353 0.9325 文献[30] 0.9992 1.0000 1.0000 0.9360 0.9685 0.8913 0.9470 0.8915 0.9362 0.9471 0.9251 0.8335 0.9694 本文算法 0.9524 0.9953 0.9970 0.9979 0.9966 0.9704 0.9785 0.9724 0.9825 0.9789 0.9780 0.9800 0.9839
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