# Edge-Aware Dual-Task Image Watermarking Against Social Network Noise
## 4 Experiments
### Ablation Study  
To verify the effectiveness of our proposed edge-aware secret hiding module (EASHM), we conducted ablation experiments on the COCO dataset. We compared two variants of our model: one with the edge-aware mechanism that performs dual-task learning of edge detection and watermark embedding simultaneously, and another without the edge-aware mechanism that only performs single-task watermark embedding. Table 7 presents the comparative results in terms of imperceptibility metrics (PSNR and SSIM) and watermark extraction accuracy averaged across various noise attacks. As shown in Table 7, incorporating the edge-aware mechanism leads to improvements across all metrics. Specifically, the dual-task learning strategy not only enhances the imper  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Our** & \multirow{2}{*}{**FlexMark**} & **ARWGAN** & **De-END** & **SSLW** & **MBRS** & **StegaStamp** & **IGA** & **HiDDeN** \\ \hline \multirow{4}{*}{**V-1**} & 0.01 & **99.98** & 99.56 & 98.18 & 99.92 & 99.16 & 98.04 & 98.46 & 96.08 & 95.75 \\  & 0.03 & **99.96** & 99.06 & 95.28 & 98.99 & 97.84 & 94.96 & 93.87 & 89.24 & 88.01 \\  & 0.05 & **99.86** & 98.72 & 93.66 & 98.34 & 96.26 & 93.19 & 90.19 & 84.92 & 84.09 \\  & Avg & **99.93** & 99.11 & 95.71 & 99.08 & 97.75 & 95.40 & 94.17 & 90.08 & 89.28 \\ \hline \multirow{4}{*}{**V-2**} & 10\% & **99.98** & 99.28 & 96.58 & 95.92 & 98.75 & 95.68 & 98.86 & 92.32 & 91.49 \\  & 30\% & **99.95** & 98.44 & 94.49 & 91.53 & 97.11 & 94.98 & 96.77 & 85.51 & 90.54 \\  & 50\% & **99.92** & 97.72 & 93.06 & 85.15 & 95.76 & 93.12 & 94.46 & 88.56 & 85.12 \\  & Avg & **99.95** & 98.48 & 94.71 & 90.87 & 97.21 & 94.59 & 96.70 & 88.80 & 89.05 \\ \hline \multirow{4}{*}{**V-3**} & 10\% & **99.95** & 98.85 & 99.80 & 98.25 & 99.58 & 95.08 & 98.20 & 94.72 & 92.93 \\  & 30\% & **99.90** & 97.72 & 98.78 & 94.75 & 98.83 & 93.47 & 96.48 & 93.14 & 90.67 \\  & 50\% & **99.77** & 96.91 & 97.21 & 92.71 & 97.16 & 91.70 & 95.26 & 92.95 & 87.34 \\  & Avg & **99.87** & 97.83 & 98.60 & 95.24 & 98.52 & 93.42 & 96.65 & 93.60 & 90.31 \\ \hline \multirow{4}{*}{**V-4**} & 90\% & **99.96** & 97.54 & 98.99 & 98.41 & 96.14 & 99.08 & 99.28 & 94.86 & 91.52 \\  & 70\% & **99.91** & 96.30 & 97.48 & 97.06 & 94.19 & 98.40 & 98.71 & 91.60 & 88.06 \\  & 50\% & **99.86** & 95.53 & 93.36 & 94.06 & 93.82 & 97.49 & 98.34 & 86.23 & 84.25 \\  & Avg & **99.91** & 96.46 & 96.61 & 96.51 & 94.72 & 98.32 & 98.78 & 90.90 & 87.94 \\ \hline \multirow{4}{*}{**V-5**} & 70 & 99.30 & 95.19 & 98.01 & **99.45** & 99.06 & 99.41 & 97.36 & 89.45 & 88.29 \\  & 60 & 99.24 & 93.72 & 96.48 & 98.79 & 98.40 & **99.28** & 96.90 & 88.08 & 85.39 \\  & 50 & **99.22** & 90.71 & 95.34 & 98.23 & 97.18 & 98.79 & 96.45 & 86.62 & 84.42 \\  & Avg & **99.25** & 93.21 & 96.61 & 98.82 & 98.21 & 99.16 & 96.90 & 88.05 & 86.03 \\ \hline \multirow{4}{*}{**V-6**} & 10\({}^{\circ}\) & **99.94** & 95.58 & 97.38 & 98.83 & 98.20 & 92.06 & 97.42 & 90.11 & 84.98 \\  & 20\({}^{\circ}\) & **99.93** & 93.07 & 96.12 & 97.14 & 97.44 & 90.30 & 96.99 & 87.86 & 78.44 \\  & 30\({}^{\circ}\) & **99.93** & 91.19 & 93.86 & 95.17 & 95.73 & 88.60 & 95.80 & 85.39 & 70.75 \\  & Avg & **99.93** & 93.28 & 95.79 & 97.05 & 97.12 & 90.32 & 96.74 & 87.79 & 78.06 \\ \hline \multirow{4}{*}{**V-7**} & 6\({}^{\circ}\) & **99.80** & 96.72 & 97.50 & 98.32 & 97.33 & 92.90 & 98.13 & 90.38 & 89.42 \\  & 8\({}^{\circ}\) & **99.80** & 95.48 & 94.15 & 96.19 & 95.40 & 88.42 & 97.37 & 87.72 & 84.51 \\ \cline{1-1}  & 10\({}^{\circ}\) & **99.79** & 94.57 & 93.71 &ceptibility of the watermarked images as indicated by higher PSNR and SSIM values, but also strengthens the robustness of watermark extraction under various noise attacks. The higher accuracy scores demonstrate that watermarks embedded along edge regions remain more stable, as these structural features serve as natural anchors that help to preserve watermark information even under distortions.