# A Zero-Watermarking Algorithm Based on Scale-Invariant Feature Reconstruction Transform
## 3 Algorithm Process
### Distinguishability Test
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Attack Mode** & **Attack Intensity** & \multicolumn{5}{c}{**Normalized Cross-Correlation**} \\ \hline  & & SIFRT & [14] & [15] & [16] & [17] & [18] & [19] \\ Median filtering & \(3\times 3\) & **0.9957** & 0.9922 & 0.6939 & **0.9974** & 0.9954 & 0.9940 & 0.9872 \\ Gaussian noise & \(0.1\) & **0.9543** & **0.9756** & 0.5961 & 0.9520 & 0.9374 & 0.9283 & 0.9455 \\ Salt and pepper noise & \(0.1\) & **0.9837** & 0.9510 & 0.6191 & **0.9748** & 0.9687 & 0.9583 & 0.9637 \\ Partial cropping & \(1/16\) & 0.9688 & 0.9360 & 0.7454 & **0.9721** & 0.9501 & **0.9792** & 0.8967 \\ Clockwise rotation & \(3^{\circ}\) & **0.9832** & 0.8223 & 0.9387 & 0.9052 & 0.8691 & **0.9794** & 0.9437 \\ Clockwise rotation & \(-3^{\circ}\) & **0.9834** & 0.8209 & 0.7020 & 0.9126 & 0.8691 & **0.9786** & 0.9623 \\ JPEG & \(10\) & 0.9164 & 0.9866 & 0.6403 & **0.9922** & 0.9749 & 0.9863 & **0.9872** \\ JPEG & \(50\) & **0.9998** & 0.9952 & 0.7151 & 0.9957 & 0.9899 & **0.9976** & 0.9949 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Robustness comparison between SIFRT algorithm and other algorithms. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Median** & **Gaussian** & **Salt and** & **Partial** & **Clockwise** & **Clockwise** & **JPEG** \\
**Filtering** & **Noise** & **Propper** & **Rotation 3\({}^{\circ}\)** & **Rotation**\(-3^{\circ}\) & **Compression** \\ \hline Butterfly & & & & & & & \\
0.9714 & 0.9523 & 0.9797 & 0.9631 & 0.9795 & 0.9801 & 0.9944 \\ \hline Squirrel & & & & & & & \\
0.9868 & 0.9541 & 0.9812 & 0.9679 & 0.9857 & 0.9859 & 0.9997 \\ \hline Flower & & & & & & & \\
0.9957 & 0.9543 & 0.9837 & 0.9682 & 0.9832 & 0.9834 & 0.9998 \\ Woman & & & & & & & \\
0.9943 & 0.9540 & 0.9807 & 0.9682 To facilitate the quantitative observation of experimental data by readers, we have bolded the maximum and submaximum values in Table 2. Based on the experiments described above, the following phenomena can be observed: The algorithm proposed in this paper exhibits NC values above 98% for resisting rotation attacks, mild compression attacks, and salt-and-pepper noise attacks, all achieving the maximum values within the group. When resisting median filtering and Gaussian noise attacks, the NC values attain the submaximum values within the group, with a difference of less than 2% compared to the maximum values, all exceeding 95%. These data indicate that the integrity of the copyright information has been well protected. However, when resisting high-intensity image compression attacks, the NC value of the algorithm decreases to 91.64%, showing a significant gap compared to other algorithms. This decrease can be attributed to a certain deviation in determining image keypoints during high-intensity image compression processing, leading to the introduction of some erroneous feature descriptor vectors in the subsequent feature matrix construction, thereby affecting the integrity of copyright information. Based on the above analysis, the algorithm proposed in this paper demonstrates a strong ability to resist most attack methods, indicating its robustness and effectively preserving the integrity of copyright information. These results align with the expectations set forth in the experimental design.