# High-Frequency Artifacts-Resistant Image Watermarking Applicable to Image Processing Models
## 5 Conclusions  
The remarkable success of DNN models underscores the increasing importance of safeguarding the IP of these advanced DNN models. To prevent potential infringements upon IP rights by DNN models, many researchers have employed model watermarking  
Figure 7: Experiment results of clean loss. The top row presents the results for “\(E\)”, and the second row displays the results for “\(E\)1”. (**a**) Marked image, (**b**) extracted results from the marked image, (**c**) clean image, (**d**) extracted results from the clean image.  
techniques. Recent efforts in this domain have been dedicated to the protection of generative models, which apply watermarking to their outputs to preserve the integrity of the host network. In this study, we find that existing generative model watermarking methods yield conspicuous high-frequency artifacts within the images generated by the generative model. These artifacts significantly impair the imperceptibility and reliability of generative model watermarking systems. This discovery highlights the need for further research and advancements in the frequency domain to enhance the capabilities of watermark concealment. In response to this challenge, we introduce an image watermarking applicable to image processing models. Our approach diverges from the traditional reliance on CNNs as watermark embedding networks. Instead, we introduce a frequency perturbation generation network designed to create low-frequency perturbations. These perturbations are then incorporated as watermarks within the low-frequency components of the carrier image. This strategy minimizes the impact of the watermark embedding process on the high-frequency attributes of the image. As a result, our approach effectively suppresses the high-frequency artifacts in the marked image, significantly improving the imperceptibility of watermarking. The proposed method achieves good image quality (>50 dB) in the spatial domain, coupled with a 100% success rate in watermark extraction under attack-free conditions. Compared with the previous method, the average frequency domain invisibility performance (FP) can be improved by up to 24.9%. It enhances the safeguarding of the IP within generative models and elevates the effectiveness and robustness of watermark extraction through adversarial training. Our work aims to contribute to the advancement of research in watermarking and promote the adoption of more effective methods for IP protection.  
Conceptualization, L.Z. and X.Z.; methodology, L.Z.; software, L.Z.; validation, L.Z.; supervision, H.W.; project administration, H.W.; funding acquisition, X.Z. 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 (NSFC) under Grants U22B2047.  
Not applicable.  
**Data Availability Statement**: Publicly available datasets were analyzed in this study. The datasets can be found here: [[https://gwern.net/danbooru2021#danbooru2019](https://gwern.net/danbooru2021#danbooru2019)]([https://gwern.net/danbooru2021#danbooru2019](https://gwern.net/danbooru2021#danbooru2019)) (accessed on 19 January 2024) and [[https://github.com/ZhangXinNan/RainDetectionAndRemoval](https://github.com/ZhangXinNan/RainDetectionAndRemoval)]([https://github.com/ZhangXinNan/RainDetectionAndRemoval](https://github.com/ZhangXinNan/RainDetectionAndRemoval)) (accessed on 19 January 2024).  
The authors declare no conflicts of interest.