# Feature-Based Image Watermarking Algorithm Using SVD and APBT for Copyright Protection
## 4 Experimental Results and Comparative Analyses
### Performance Comparisons
In [32], the watermark is embedded into singular values of the DCT coefficients matrix, which is derived from four segmented sub-images in the DWT domain. As can be seen in Table 2, owing to the large number of feature points, the watermark information is embedded into the points neighborhood sparsely, which leads to a high PSNR value of the proposed scheme. Due to the block partition in the algorithm, the execution time will be higher. Additionally, the proposed watermarking method has better performance than previous watermarking schemes under some common image attacks. In [32], the host image is divided into four sub-images manually for watermark embedding, resulting in better robustness to JPEG compression. Moreover, the watermarked image will suffer various types of attacks in the transmission procedures. The performance of the APDCBT-based algorithm is presented in Figure 8, in which (a-f) are watermarked images and extracted watermarks based on APDCBT after five types of attacks: Gaussian noise (0, 0.01), salt and pepper noise (0.01), JPEG compression (QF = 100), cropping the sparse region (100 \(\times\) 100), and cropping the dense region (100 \(\times\) 100), which are labeled as 2 to 6, respectively. Furthermore, the result with no attack is labeled as 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Images**} & **PSNR** & **NC** & **BER** \\ \hline \multirow{2}{*}{Vogue} & \(w_{1}\) & 77.78 & 0.7928 & 0.0969 \\  & \(w_{2}\) & 83.29 & 0.8515 & 0.0434 \\ \hline \multirow{2}{*}{Cablecar} & \(w_{1}\) & 86.30 & 0.8577 & 0.0770 \\  & \(w_{2}\) & 90.28 & 0.9044 & 0.0342 \\ \hline \multirow{2}{*}{Canyon} & \(w_{1}\) & 85.98 & 0.8299 & 0.0784 \\  & \(w_{2}\) & 93.29 & 0.8918 & 0.0400 \\ \hline \multirow{2}{*}{Clown} & \(w_{1}\) & 81.42 & 0.8208 & 0.1056 \\  & \(w_{2}\) & 87.69 & 0.8281 & 0.0489 \\ \hline \multirow{2}{*}{Cornfield} & \(w_{1}\) & 86.63 & 0.8618 & 0.0853 \\  & \(w_{2}\) & 84.19 & 0.8949 & 0.0365 \\ \hline \multirow{2}{*}{Frog} & \(w_{1}\) & 82.54 & 0.8270 & 0.0842 \\  & \(w_{2}\) & 85.07 & 0.8820 & 0.0430 \\ \hline \multirow{2}{*}{Fruits} & \(w_{1}\) & 75.17 & 0.8879 & 0.0784 \\  & \(w_{2}\) & 81.49 & 0.9217 & 0.0291 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Different Indexes** & **[PERSON] et al. [30]** & **[PERSON] et al. [31]** & **[PERSON] and [PERSON] [32]** & **APDCBT** \\ \hline PSNR (dB) & 20.98 & 39.75 & 56.42 & 90.56 \\ Execution Time (s) & 1.10 & 2.43 & 3.46 & 6.07 \\ Gaussian Noise (0, 0.01) & 0.9695 & 0.8016 & 0.9897 & 0.9983 \\ Salt and Pepper Noise (0.01) & 0.9157 & 0.8925 & 0.9842 & 0.9923 \\ JPEG Compression (QF = 50) & 0.9877 & 0.9850 & 0.9865 & 0.9830 \\ JPEG Compression (QF = 100) & 0.9960 & 0.9984 & 0.9999 & 0.9838 \\ Sparse Region Cropping (100 \(\times\) 100) & 0.8236 & 0.9236 & 0.9999 & 0.9841 \\ Sparse Region Cropping (256 \(\times\) 256) & - & 0.8187 & 0.9602 & 0.9821 \\ Dense Region Cropping (100 \(\times\) 100) & 0.6589 & 0.9255 & 0.9804 & 0.9847 \\ Dense Region Cropping (256 \(\times\) 256) & - & 0.8182 & 0.9682 & 0.9881 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of watermarking algorithms based on APDCBT and previous schemes under different attacks. Figure 9 shows watermarked images and extracted watermarks in the APDCBT-based watermarking scheme under combinations of the above attacks. Both Figures 8 and 9 demonstrate that the proposed watermarking scheme can extract the watermark with high quality, which can also suggest robustness of the proposed method. Figure 8: Watermarked images and extracted watermarks based on APDCBT under five types of attacks: (**a**) no attack; (**b**) Gaussian noise (0, 0.01); (**c**) salt and pepper noise (0.01); (**d**) JPEG compression (QF = 100); (**e**) cropping the sparse region (100 \(\times\) 100); (**f**) cropping the dense region (100 \(\times\) 100). Figure 9: \(Cont.\)  
Additionally, Table 3 illustrates the robustness of the proposed algorithm under combinations of different attacks.