# Robust Image Watermarking Algorithm Based on ASIFT against Geometric Attacks
## 5 Experimental Results and Discussion
### Results and Discussion
#### 5.2.2 Performance of Robustness  
To test the robustness of the proposed watermarking scheme thoroughly, common attacks and geometric attacks are performed on the watermarked images. Here, the image labelled 'Elaine' is selected as the specific illustration. Figure 10 gives the attacked images and extracted watermarks under different kinds of processes and attacks, which contains JPEG (50 and 70), salt and pepper noise with densities of 0.01 and 0.05, speckle noise with density of 0.01 and 0.1, Gaussian noise with the mean of 0 and the variances of 0.05 and 0.1, median filter with the window sizes of 3 \(\times\) 3, 4 \(\times\) 4, and 5 \(\times\) 5, and center cropping (25%). \begin{table}
\begin{tabular}{c c} \hline \hline
**Watermarked Images** & **Number of Points** \\ \hline Airplane & 14,184 \\ Elaine & 9864 \\ Lena & 10,068 \\ Mountain & 25,833 \\ Bank & 11,728 \\ Peppers & 8651 \\ Milk drop & 6674 \\ Gold hill & 17,073 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Number of affine-scale-invariant feature transform (ASIFT) feature points. Figure 10: _Cont._  
Figure 10: Attacked images and extracted watermarks under different kinds of attacks: (**a**) JPEG (70); (**b**) extracted watermark from (**a**); (**c**) JPEG (50); (**d**) extracted watermark from (**c**); (**e**) salt and pepper noise (0.01); (**f**) watermark extracted from (**e**); (**g**) salt and pepper noise (0.05); (**h**) extracted watermark from (**g**); (**i**) speckle noise (0.01); (**j**) extracted watermark from (**i**); (**k**) speckle noise (0.1); (**l**) extracted watermark from (**k**); (**m**) Gaussian noise (0, 0.01); (**n**) extracted watermark from (**m**); (**o**) Gaussian noise (0, 0.05); (**p**) extracted watermark from (**o**); (**q**) median filter (3 \(\times\) 3); (**r**) extracted watermark from (**q**); (**s**) median filter (4 \(\times\) 4); (**t**) extracted watermark from (**s**); (**u**) median filter (5 \(\times\) 5); (**v**) extracted watermark from (**u**); (**w**) center cropping (25%); (**x**) extracted watermark from (**w**). In Figure 11, attacked images and extracted watermarks under different kinds of geometric attacks are given, including scaling with parameters of 0.25, 0.5, 0.9, and 1.2, rotation with parameters of \(5^{\circ}\), \(10^{\circ}\), \(30^{\circ}\), and \(45^{\circ}\), and translation with parameters of 128 pixels and 256 pixels in horizontal and vertical directions. Figure 11: _Cont._  
Tables 2 and 3 give the robustness performance of the proposed scheme on different images, and the image labelled 'Elaine' with less points as well as the image labelled 'Gold hill' with more points are selected as the representatives. As can be seen in Table 2, the robustness performance of the proposed algorithm is given on the host image 'Elaine' and the watermark image 'SDU'. The PSNR value of the proposed method is 52.57 dB, which illustrates the good transparency of the watermarked image. In Table 3, the robustness performance of the proposed algorithm is given on the host image 'Gold hill' and the watermark image 'SDU'. The PSNR value is 51.01 dB. In general, the image labelled 'Gold hill' has more points than the image labelled 'Elaine'. Thus, the robustness to geometric attacks on 'Gold hill' is a little better than that on 'Elaine'.