# Two-Stage Robust Lossless DWI Watermarking Based on Transformer Networks in the Wavelet Domain
## 5 Conclusions  
We proposed a two-stage deep learning-based robust lossless watermarking algorithm for DWI images by combining the advantages of robust and reversible watermarking. In the first stage, we pre-trained a robust watermarking network using a framework based on DWT and Transformer. The watermark redundancy was embedded into the decomposed low-frequency components, and an attention module was designed to effectively fuse multi-frequency features for a better reconstruction performance. In the second stage, the compensation information and image hash value were embedded into the watermarked image using reversible watermarking techniques. The image hash value was utilized to detect whether the image had been attacked, and under the condition of no attack, lossless recovery of the cover image was achieved using the compensation information, ensuring the integrity of the medical image data. The proposed algorithm enabled effective copyright protection for DWI images by extracting the watermark for copyright authentication under various types of attacks. Currently, the embedded watermark image was fixed and unchanged. In the future, the use of random watermarks can be considered to enhance security. The robustness of the watermark is designed to address known attacks, and in the future, adversarial training can be employed to enhance robustness against unknown attacks.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Watermarked** & **[13]** & **[17]** & **[10]** & **[11]** & **[12]** & **Proposed** \\
**Image** & & & & & & \\ \hline PSNR/dB & 39.08 & 58.69 & 43.7 & 43.1 & 46.8 & 60.18 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison with the PSNR of watermarked images from other methods.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Attack** & **[13]** & **[17]** & **[10]** & **[11]** & **[12]** & **Proposed** \\ \hline GN (0.01) & 0.002 & 0.000 & 0.000 & 0.010 & 0.000 & 0.000 \\ GN (0.02) & 0.024 & 0.000 & 0.000 & 0.060 & 0.010 & 0.000 \\ GN (0.03) & 0.025 & 0.026 & 0.000 & 0.180 & 0.050 & 0.000 \\ JPEG (00) & 0.164 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 \\ JPEG (70) & 0.333 & 0.012 & 0.000 & 0.000 & 0.000 & 0.000 \\ JPEG (30) & 0.394 & 0.343 & 0.000 & 0.000 & 0.000 & 0.000 \\ JPEG (10) & 0.430 & 0.375 & 0.150 & 0.000 & 0.000 & 0.002 \\ rotation (5) & 0.004 & 0.000 & — & 0.000 & 0.000 & 0.000 \\ rotation (15) & 0.009 & 0.010 & — & 0.000 & 0.000 & 0.002 \\ rotation (30) & 0.011 & 0.010 & — & 0.000 & 0.000 & 0.001 \\ rotation (45) & 0.011 & 0.020 & — & 0.000 & 0.000 & 0.001 \\ scaling (0.5) & 0.063 & 0.001 & — & 0.000 & 0.000 & 0.000 \\ scaling (0.75) & 0.096 & 0.010 & — & 0.006 & 0.002 & 0.000 \\ scaling (1.25) & 0.131 & 0.010 & — & 0.000 & 0.000 & 0.000 \\ scaling (1.5) & 0.150 & 0.015 & — & 0.000 & 0.030 & 0.000 \\ scaling (2) & 0.206 & 0.025 & — & 0.020 & 0.010 & 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Robustness comparison with other methods.  
**Author Contributions:** Conceptualization, methodology, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]); validation, [PERSON] ([PERSON]) and [PERSON]; software, data curation, D.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China: 62062023, Guizhou Science and Technology Plan Project: ZK[2021]-YB314.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** [[https://db.humanconnectome.org/](https://db.humanconnectome.org/)]([https://db.humanconnectome.org/](https://db.humanconnectome.org/)) (accessed on 17 April 2023).  
**Conflicts of Interest:** The authors declare no conflict of interest.