# DARI-Mark: Deep Learning and Attention Network for Robust Image Watermarking
## 5 Conclusions  
In this paper, we proposed a robust image watermarking framework called DARI-Mark. Considering the masking effect of the HVS, an attention network was introduced in the end-to-end watermarking framework. Through the setting of the loss function, the attention network in the watermark embedding network helped the network automatically select texturally complex regions to embed the watermark for improving imperceptibility. During watermark extraction, the attention module assigns larger attention weights to features containing watermarks and smaller weights to noisy signals, increasing the robustness of watermark extraction. In addition, to avoid the phenomenon of gradient disappearance or explosion when the network is deep, DARI-Mark added residual modules. Experiments \begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **PSNR (dB)** \\ \hline [PERSON] et al. [24] & 35.46 \\ ReDMark [25] & 35.93 \\ DARI-Mark & 37.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: PSNR values of three methods. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Attack**} & \multicolumn{2}{c}{**[PERSON] et al. [24]**} & \multicolumn{2}{c}{**ReDMark [25]**} & \multicolumn{2}{c}{**DARI-Mark**} \\ \cline{3-8}  & & **BER (\%)** & **NCC** & **BER (\%)** & **NCC** & **BER (\%)** & **NCC** \\ \hline JPEG & \(Q=50\) & 8.06 & 0.9055 & 3.03 & 0.9698 & **1.36** & **0.9832** \\ com- & \(Q=70\) & 4.24 & 0.9601 & 1.60 & 0.9799 & **0.00** & **1.0000** \\ pression & \(Q=90\) & 0.96 & 0.9988 & 0.00 & 1.0000 & **0.00** & **1.0000** \\ \hline \multirow{3}{*}{\begin{tabular}{c} Sharp- \\ ening \\ \end{tabular} } & \(R_{\text{S}}=1\) & 0.98 & 0.9894 & 0.95 & 0.9892 & **0.00** & **1.0000** \\  & \(R_{\text{S}}=5\) & 1.72 & 0.9799 & 1.47 & 0.9823 & **0.00** & **1.0000** \\  & \(R_{\text{S}}=10\) & 2.01 & 0.9763 & 3.42 & 0.9599 & **0.00** & **1.0000** \\  & \(R_{\text{S}}=20\) & 3.51 & 0.9575 & 4.98 & 0.9457 & **0.19** & **0.9998** \\ \hline \multirow{3}{*}{\begin{tabular}{c} Cropping \\ \end{tabular} } & \(c=0.10\) & 4 & 0.954 & 2.14 & 0.974 & **0.68** & **0.9917** \\  & \(c=0.20\) & 5.46 & 0.9386 & 5.33 & 0.9321 & **3.81** & **0.9499** \\  & \(c=0.30\) & 17.10 & 0.8216 & 8.67 & 0.9008 & **4.05** & **0.9567** \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Gaut- \\ sian \\ noise \\ \end{tabular} } & \(\sigma=0.05\) & 5.99 & 0.9706 & 0.30 & 0.9943 & **0.09** & **0.9988** \\  & \(\sigma=0.15\) & 27.00 & 0.6802 & 5.08 & 0.9399 & **3.71** & **0.9563** \\  & \(\sigma=0.25\) & 38.17 & 0.5702 & 12.73 & 0.8571 & **10.30** & **0.9088** \\ \hline \multirow{3}{*}{Scaling} & \(r=0.50\) & 10.98 & 0.8796 & **5.92** & **0.9711** & 14.47 & 0.8343 \\  & \(r=0.75\) & 3.36 & 0.9599 & **0.00** & **1.0000** & 5.56 & 0.9399 \\  & \(r=1.50\) & 1.53 & 0.9832 & **0.00** & **1.0000** & 0.48 & 0.9953 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Salt-and- \\ pepper \\ noise \\ \end{tabular} } & \(s=0.04\) & 0.29 & 0.9965 & 1.45 & 0.9802 & **0.00** & **1.0000** \\  & \(s=0.06\) & 1.53 & 0.9832 & 1.17 & 0.9858 & **0.00** & **1.0000** \\  & \(s=0.10\) & 3.19 & 0.9621 & 3.51 & 0.9600 & **0.00** & **1.0000** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: BER and NCC comparison results of three methods under multiple attacks. showed that DARI-Mark had a good imperceptibility and robustness to multiple attacks. Compared with other state-of-the-art watermarking methods, DARI-Mark was more robust to JPEG compression, sharpening, cropping, Gaussian noise, and salt-and-pepper noise attacks. However, the robustness of DARI-Mark against geometric attacks (such as scaling, rotation, etc.) was not ideal. In future research work, we will improve the framework to make it robust against more types of image distortion attacks. [PERSON] and [PERSON] conceived the algorithm and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the results; [PERSON] drafted the manuscript; [PERSON], [PERSON] and [PERSON] revised the manuscript.