# Edge-Aware Dual-Task Image Watermarking Against Social Network Noise
## 4 Experiments
### Imperceptibility Analysis  
To evaluate the imperceptibility of our proposed watermarking scheme, we conducted comprehensive experiments using two widely adopted metrics, PSNR and SSIM, across three benchmark datasets: COCO [51], Div2K [52], and WikiArt [53]. As shown in Table 3, both metrics were calculated by averaging over all images in the test sets to ensure a comprehensive evaluation of the visual quality. The experimental results demonstrate the superior performance of our model in terms of visual quality preservation. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline
**Methods** & **Our Model** & **FlexMark** & **ARWGAN** & **De-END** & **SSIM** & **MBRS** & **StegAtom** & **IGA** & **HiDDeN** \\ \hline Metric & PSNR & SSIM & PSNR & SSIM & PSNR & SSIM & PSNR & SSIM & PSNR & SSIM & PSNR & SSIM & PSNR & SSIM \\ \hline COCO & **40.82** & **0.9667** & 30.89 & 0.9152 & 35.72 & 0.9672 & 39.51 & 0.9794 & 33.92 & 0.8702 & 35.82 & 0.8676 & 34.48 & 0.9558 & 32.11 & 0.8660 & 32.53 & 0.8959 \\ \hline _Di2K_ & **41.97** & **0.9882** & 30.72 & 0.9424 & 36.80 & 0.9813 & 41.34 & 0.9871 & 35.01 & 0.8812 & 37.13 & 0.8943 & 35.50 & 0.9626 & 34.39 & 0.8745 & 34.13 & 0.8623 \\ \hline _WikiArt_ & **41.52** & **0.9887** & 30.53 & 0.9231 & 35.98 & 0.9704 & **41.59** & 0.9842 & 35.10 & 0.8772 & 36.43 & 0.8992 & 35.47 & 0.9648 & 34.02 & 0.8719 & 33.84 & 0.8614 \\ \hline
1 st Count & \multicolumn{2}{c}{**5**} & \multicolumn{2}{c}{0} & \multicolumn{2}{c}{0} & \multicolumn{2}{c}{**1**} & \multicolumn{2}{c}{0} & \multicolumn{2}{c}{0} & \multicolumn{2}{c}{0} & \multicolumn{2}{c}{0} & \multicolumn{2}{c}{0} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of PSNR (dB) and SSIM metrics on COCO, Div2K, and WikiArt datasets. **Bold**: the best results, underline: the second best. On the COCO [51] dataset, our model achieves a remarkable PSNR of 40.82 dB and SSIM of 0.9867, significantly outperforming all competing methods. The second-best performance is achieved by De-END [15], with a PSNR of 39.51 dB and SSIM of 0.9794, while our method still maintains a noticeable lead of approximately 1.31 dB in PSNR and 0.0073 in SSIM. This consistent advantage suggests that our approach introduces minimal visual artifacts while embedding watermarks. The superiority of our approach is further validated on the Div2K [52] dataset, where we attain the highest PSNR of 41.97 dB and SSIM of 0.9882. These metrics again surpass the second-best method (De-END [15]), which achieves a PSNR of 41.14 dB and SSIM of 0.9871. Although the performance gap is relatively smaller on this high-resolution dataset (0.83 dB in PSNR), it consistently demonstrates our model's capability to preserve fine details and textures during the watermarking process. Similar trends are observed on the WikiArt [53] dataset, where our method achieves a PSNR of 41.52 dB and an SSIM of 0.9887, maintaining consistent superiority across different artistic styles and compositions. De-END [15] again ranks second, with a PSNR of 39.98 dB and SSIM of 0.9842, further confirming the robust performance advantage of our approach across diverse visual content. Notably, our method consistently ranks first across all three datasets in both metrics, as evidenced by the \"1 st Count\" row showing the five top performances. While De-END demonstrates strong performance as the second-best method, our approach maintains a clear advantage across all experimental settings. This comprehensive superiority in imperceptibility metrics suggests that our proposed approach successfully addresses one of the fundamental challenges in digital watermarking: maintaining high visual quality while embedding hidden information. The consistently high SSIM values (all above 0.98) particularly indicate that our method preserves the structural information and perceptual quality of the original images to a remarkable degree.