## 5 Conclusions  
We propose an end-to-end, screen-shooting-resilient image watermarking framework using convolution neural network in this study. Our framework consists of four components, namely, embedding, transform sub-network, simulating attack layers and extracting components. The embedding component is used to embed the watermark into the DCT coefficients of images. The transform sub-network modifies the image to obtain the DCT coefficient feature of the image. The simulating attack layer is designed to simulate a series of distortions, such as perspective transform, optical distortion, light distortion, JPEG distortion and moire pattern, in the screen-shooting process. We design a moire attack network for simulating the moire phenomenon, which is the most common screen-shooting attack, to improve the resiliency of the watermarked image against distortions in real screen-shooting scenes. The extracting component extracts the watermark from the captured photos. In order to balance the robustness and imperceptibility of the algorithm, we introduce a loss function based on NC and SSIM. From the experiments, it can be concluded that SSDeN employs a distributed embedding strategy with greater concentration on low- and middle-frequency coefficients in keeping with the traditional method's results. Simultaneously, the above shows the rationality of our proposed framework. \begin{table}
\begin{tabular}{p{113.8 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Methods** & **StegaStamp [11]** & **HiDDeN [13]** & **ReDMark [3]** & **TSDL [12]** &
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt}} **The Proposed (SS-DeN)** \\ \end{tabular} \\ \hline Cropping (10) & 0.7697 & 0.8823 & **0.9815** & 0.8501 & 0.9710 \\ \hline JPEG (50) & 0.7952 & 0.7731 & 0.9779 & 0.7770 & **0.9798** \\ \hline Blurring (1.2) & 0.9142 & 0.7553 & 0.7871 & **0.9956** & 0.9850 \\ \hline Scaling (2.0) & 0.9987 & NULL & **0.9995** & 0.9233 & 0.9800 \\ \hline Sharpening (4) & NULL & NULL & 0.9981 & 0.9900 & **1.0** \\ \hline Perspective & **0.9897** & NULL & NULL & NULL & 0.9640 \\ \hline Color changing & 0.9878 & NULL & NULL & **0.9886** & NULL \\ \hline Noise (10) & 0.9523 & 0.9212 & **0.9889** & 0.8995 & **1.0** \\ \hline Moire (U-Net) & 0.8952 & NULL & NULL & NULL & **0.9926** \\ \hline Visible watermark (15) & 0.9700 & NULL & **0.9980** & 0.9567 & 0.9975 \\ \hline Light & **0.9994** & NULL & NULL & 0.9929 & 0.9899 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparing the extracted watermark of the proposed algorithm and previous methods in terms of NC. Our follow-up investigation will continue to improve and extend SSDeN. Specifically, we will simulate additional capture devices and increasingly complex shooting scenes to improve watermark detection in real-world scenarios. Screen-shooting-resilient watermarking can also be extended for color images while fully fitting all real screen-shooting scenarios. Conceptualization, S.Z. and R.B. ; methodology, L.L. ; software, R.B. ; validation, R.B., L.L. and S.Z. ; formal analysis, C.-C.C. ; investigation, R.B. ; resources, R.B. ; data curation, R.B. ; writing--original draft preparation, C.-C.C. ; writing--review and editing, L.L. ; visualization, J.L. ; supervision, R.B. ; project administration, R.B. ; funding acquisition, L.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (No. 62172132) and the Public Welfare Technology Research Project of Zhejiang Province (No. LGF21F020014). Not applicable. Thank you to the reviewers who reviewed this paper and the MDPI editor who edited it professionally. The authors declare no conflict of interest. Not applicable. The following abbreviations are used in this manuscript: