## 5 Experimental Results and Discussion
### Objective Evaluation of the Proposed Watermarking Model
To evaluate its generalization capability against diverse image attacks, we test the system using twelve types of untrained noises: Gaussian noise, JPEG Compression, Cropping, Dropout, Salt and Pepper noise, Rotation, Median filter, Adjust Brightness, Adjust Contrast, Image scaling, Shearing attack, and Histogram equalization. In the robustness assessment of the suggested work presented in Table 2, our FIAE-GAN model demonstrates superior performance compared to HiDDeN across various noise attacks. Specifically, for Gaussian noise, the FIAE-GAN model exhibits nearly perfect performance with an accuracy of 96.85% at a noise density of 0.10, whereas HiDDeN's accuracy significantly decreases to 76.81%. Correspondingly, in the context of JPEG compression, a notable difference is observed: HiDDeN achieves an accuracy of 73.39% at a quality factor (QF) of 10. At the same time, FIAE-GAN performs markedly better with an accuracy of 94.12%. The performance gaps are particularly pronounced in several attack scenarios. Under a Dropout attack with a dropout rate of 0.3, FIAE-GAN achieves an accuracy of 99.87%, compared to HiDDeN's accuracy of 87.91%. Similarly, during a shearing attack with parameters [0.4, 0.4], FIAE-GAN attains an accuracy of 99.36%, whereas HiDDeN's accuracy is considerably lower at 76.47%. In the case of rotation at 90\({}^{\circ}\), HiDDeN's accuracy is only \begin{table}
\begin{tabular}{l l l} \hline \hline
**Model** & **PSNR** & **SSIM** \\ \hline HiDDeN [8] & 32.14 & 0.9315 \\ \hline TSDL [47] & 33.50 & - \\ \hline DA [46] & 33.70 & - \\ \hline MBRS [48] & 35.08 & 0.8914 \\ \hline ReDMark [7] & 35.93 & 0.9660 \\ \hline FIAE-GAN & 35.89 & 0.9679 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of different models on CCTSDB. 72.94%, whereas FIAE-GAN achieves a significantly higher accuracy of 98.12%. For Salt and Pepper noise at a density of 0.1, HiDDeN's accuracy is 77.56%, while FIAE-GAN maintains a substantially higher accuracy of 97.57%. Furthermore, with contrast adjustment at a factor of 2.0, HiDDeN's accuracy drops to 72.45%, compared to FIAE-GAN's much higher accuracy of 96.87%. Overall, FIAE-GAN demonstrates robust and reliable performance across various types of noise attacks, showing particularly significant improvements over HiDDeN in handling high noise densities and challenging distortions. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Attack** & **Noise Density** & **HiDDeN [.\%]** & **FIAE-GAN [.\%]** \\ \hline \multirow{3}{*}{Gaussian noise} & 0.001 & 85.57 & 100.00 \\  & 0.05 & 80.23 & 98.13 \\  & 0.10 & 76.81 & 96.85 \\ \hline \multirow{3}{*}{JPEG Compression} & QF = 10 & 73.39 & 94.12 \\  & QF = 50 & 78.67 & 96.49 \\  & QF = 90 & 95.51 & 99.21 \\ \hline Cropping & [20, 20, 420, 420] & 78.82 & 98.08 \\ \hline Dropout & 0.3 & 87.91 & 99.87 \\ \hline \multirow{3}{*}{Salt and Pepper} & 0.001 & 91.34 & 99.84 \\  & 0.05 & 82.73 & 99.31 \\  & 0.1 & 77.56 & 97.57 \\ \hline \multirow{3}{*}{Rotation} & 45\({}^{\circ}\) & 78.81 & 98.53 \\  & 90\({}^{\circ}\) & 72.94 & 98.12 \\ \hline \multirow{3}{*}{Median filter} & [2, 2] & 88.39 & 99.64 \\  & [3, 3] & 81.27 & 97.41 \\ \hline \multirow{3}{*}{Adjust Brightness} & 1.1 & 92.61 & 98.94 \\  & 1.3 & 86.38 & 97.37 \\ \hline \multirow{3}{*}{Adjust Contrast} & 1.0 & 82.92 & 97.52 \\  & 2.0 & 72.45 & 96.87 \\ \hline \multirow{3}{*}{Image scaling} & 0.5 & 75.14 & 97.23 \\  & 2 & 78.45 & 96.61 \\ \hline Shearing attack & [0.4, 0.4] & 76.47 & 99.36 \\ \hline Histogram equalization & 1.0 & 86.37 & 99.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Robustness assessment of the suggested work. Figure 11: Comparative analysis of proposed work with HiDDeN [8], TSDL [47], MBRS [48], ReDMark [7].