# Edge-Aware Dual-Task Image Watermarking Against Social Network Noise
## 5 Conclusions  
In this study, we present a novel edge-aware dual-task watermarking framework that effectively addresses the fundamental trade-off between visual quality and robustness in digital image watermarking. Through the innovative combination of edge detection and watermark embedding in a maximally parameter-shared encoder, our framework demonstrates the potential of leveraging structural features for more natural and robust watermark placement. The incorporation of knowledge distillation from an edge detection teacher further enhances the framework's ability to preserve image structural integrity during watermark embedding.  
Our comprehensive defense mechanism, consisting of the social network noise simulator and secret recovery module, proves effective in handling real-world challenges in social media image sharing. The SNNS successfully simulates various types of noise and transformations commonly encountered on social platforms, while the SRM, equipped with spatial transformation capabilities, enables reliable watermark extraction even under significant geometric distortions. This dual-component design significantly enhances the practical applicability of our watermarking system.  
Extensive experimental results validate the effectiveness of our proposed framework, demonstrating superior watermark imperceptibility, with PSNR and SSIM values exceeding 40.82 dB and 0.9867, respectively, while maintaining an over 99% decoding accuracy under seven types of attacks with three intensity levels. Compared to existing methods, our approach achieves superior performance in both watermark imperceptibility and robustness against various attacks. The edge-aware embedding strategy, combined with the dual-task learning mechanism, demonstrates particular strength in preserving image quality while maintaining robust watermark extraction capabilities. However, limitations exist in computational complexity and performance under extreme compression scenarios. Additionally, our edge-aware embedding approach faces certain limitations when processing images with minimal structural features or low texture complexity. For images with smooth regions and subtle edges, such as sky backgrounds, gradient fills, and out-of-focus photographs, the algorithm may struggle to effectively embed watermarks due to the reduced availability of suitable edge features. This can lead to decreased watermark robustness and potential degradation in visual quality when forced to modify relatively uniform areas. Future improvements should focus on model optimization and enhancing robustness against advanced attacks. Beyond these improvements, we will extend our framework to video watermarking and other multimedia domains, with a particular focus on addressing the challenges posed by diverse media attacks and temporal consistency in dynamic content.  
Conceptualization, methodology, H.J., J.W. and Y.Y.; validation, J.W., Y.Y. and X.L.; resources, F.K.; data curation, X.T.; writing--original draft, J.W. and X.L.; writing--review and editing, H.J., F.K. and L.Q.; visualization, H.J., J.W. and X.T.; supervision, project administration, F.K.;funding acquisition, H.J., F.K. and L.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research and Development Program Young Scientists Project of Research on Application Technology of Property Right Evaluation and Right Confirmation Mark of Cultural Products (Grant No. 2021 YFF0900200), and by the Basic Scientific Research Operating Expenses of the Academy of Broadcasting Science, NRTA (JBKY20240190).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.