# Robust Image Watermarking Algorithm Based on ASIFT against Geometric Attacks
## 6 Conclusions  
With the rapid development of image processing technology, altering the content of an image is easy for everyone, but may be illegal for an attacker. It is urgent and necessary to overcome this problem, to protect the integrity and authenticity of images. Watermarking is a powerful technique proposed to solve this problem. In this paper, an ASIFT-based robust watermarking algorithm is proposed to solve the geometric attacks in copyright protection issues. In the proposed algorithm, the watermark information is inserted into the low frequency sub-band after DWT three times, where the SVD is performed again for watermark embedding. While in the extraction process, to improve the robustness to geometric attacks, ASIFT is applied to obtain feature points which are invariant to geometric attacks. Then, features of acquired points between the watermarked image and received image are used to realize the resynchronization to improve the robustness. Experimental results show that the proposed algorithm achieves great balance between robustness and imperceptibility, and is  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Attacks** & **SIFT**[31] & **ASIFT** \\ \hline Scaling 0.25 & 0.9832 & 0.9904 \\ Scaling 0.5 & 0.9889 & 0.9969 \\ Scaling 0.9 & 0.9901 & 0.9961 \\ Scaling 1.2 & 0.9871 & 0.9911 \\ Rotation 2\({}^{\circ}\) & 0.9887 & 0.9704 \\ Rotation 5\({}^{\circ}\) & 0.9918 & 0.9542 \\ Rotation 10\({}^{\circ}\) & 0.9954 & 0.9784 \\ Rotation 30\({}^{\circ}\) & 0.9954 & 0.9685 \\ Rotation 45\({}^{\circ}\) & 0.9941 & 0.9608 \\ Horizontally translation (256 pixels) & 0.9847 & 0.9974 \\ Vertically translation (256 pixels) & 0.9847 & 0.9974 \\ Horizontally translation (128 pixels) & 0.9853 & 0.9978 \\ Vertically translation (128 pixels) & 0.9853 & 0.9978 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparisons with previous algorithms.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Common Attacks** & **NC** & **Geometric Attacks** & **NC** \\ \hline No attacks & 0.9978 & Scaling 0.25 & 0.9910 \\ JPEG (50) & 0.9964 & Scaling 0.5 & 0.9972 \\ JPEG (70) & 0.9973 & Scaling 0.9 & 0.9968 \\ Median filter (3 \(\times\) 3) & 0.9955 & Scaling 1.2 & 0.9943 \\ Median filter (4 \(\times\) 4) & 0.9885 & Rotation 2\({}^{\circ}\) & 0.9802 \\ Median filter (5 \(\times\) 5) & 0.9896 & Rotation 5\({}^{\circ}\) & 0.9867 \\ Gaussian noise (0, 0.05) & 0.9253 & Rotation 10\({}^{\circ}\) & 0.9909 \\ Gaussian noise (0, 0.1) & 0.8492 & Rotation 30\({}^{\circ}\) & 0.9893 \\ Speckle noise (0.01) & 0.9921 & Rotation 45\({}^{\circ}\) & 0.9909 \\ Speckle noise (0.1) & 0.9670 & Horizontally translation (256 pixels) & 0.9973 \\ Salt and pepper noise (0.01) & 0.9835 & Vertically translation (256 pixels) & 0.9973 \\ Salt and pepper noise (0.05) & 0.9507 & Horizontally translation (128 pixels) & 0.9976 \\ Center cropping (25\%) & 0.8597 & Vertically translation (128 pixels) & 0.9976 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Robustness of the proposed algorithm on image Gold hill.  
robust against geometric attacks, JPEG compression, noise addition, copping, filter operation, and so on. In the future work, SIFT correction can be applied to video watermarking for rotation resistance.  
This work was supported by the National Natural Science Foundation of China (No. 61702303, No. 61201371); the Natural Science Foundation of Shandong Province, China (No. ZR2017 MF020, No. ZR2015 PF004); and the Research Award Fund for Outstanding Young and Middle-Aged Scientists of Shandong Province, China (No. BS2013 DX022).  
[PERSON] and [PERSON] conceived the algorithm and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the results; [PERSON] drafted the manuscript; [PERSON], [PERSON] and [PERSON] revised the manuscript. All authors read and approved the final manuscript.  
The authors declare no conflict of interest.