## 4 Proposed Methodology
But, our resultant SSIM is greater than all of the existing methods. However, this section compares the imperceptibility and robustness of our proposed method with other hybrid watermarking methods. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Host Image** & **Watermark Image** & **Bit Depth** & **PSNR (dB)** & **SSIM** \\ \hline \multirow{3}{*}{Pepper} & \multirow{3}{*}{Copyright} & 1024, 512, 256, 128, 64, 32, 16 & 47.5334 & 0.9984 \\ \cline{3-4}  & & 8 & 48.8636 & 0.9988 \\ \cline{1-1} \cline{3-4}  & & 2, 4 & Infinite & \(1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Capacity Analysis by PSNR(dB) and SSIM. Figure 11: Extracted watermark; (**a**) with right key 32; (**b**) with wrong key. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline



## 6 Conclusions and Future Work

This paper proposed a hybrid robust invisible image watermarking by combining DWT and SVD. The proposed method is tested on different groups of host images, and the performance is compared with recent methods in the literature. It ensures an effective image authentication as it uses invisible image watermarking. It is observed that the proposed method showed superiority over the existing methods in terms of imperceptibility and robustness. Our proposed method ensures better imperceptibility, which is 48.9688 dB. Compared to the existing advanced methods, the method also provides enhanced robustness against noise, filter, and image blur attacks. For noise attacks, average NC values are close to 1 and greater than other existing methods. For filter attacks, our average NC value is 0.9876, which is greater than the existing methods. For image blur attack, we found higher NC value of 0.9981 than other methods. The Arnold map increases the system's security. The original watermark and decrypted watermark are identical for our method as the SSIM value of these two images is 1. DWT reduces the inserting capacity into the cover or host image and also leads to failure in extracting the watermark image due to its shift variance property. In the future, we will work with redundant DWT to overcome these issues. The FPD problem, however, is a limitation of the suggested technique and is not addressed in this study. In future work, we will address this issue and work with other intentional and unintentional attacks along with removal and brute-force attacks in conjunction with genetic and neural network-based optimization techniques, in order to test the system's robustness and implement our method for any watermark image size, while simultaneously maintaining proper balance among basic design requirements. Also, we will use deep learning and diverse interpolation-based methods for geometric attacks like image resizing, rotation, and cropping. In blind watermarking, the watermark can be accurately extracted from the watermarked image while preserving the quality and integrity of the host image. To achieve a blind watermarking capability is essential for practical applications, as the original image is not always feasible or available. In the future,

Figure 14: Comparison of Robustness (**a**) for MF attack; (**b**) for LPGF attack. \begin{table}
\begin{tabular}{c c} \hline \hline
**Reference** & **SPN** \\ \hline [PERSON] et al., 2021 [3] & 0.9912 \\ \hline [PERSON] et al., 2021 [17] & 0.8734 \\ \hline [PERSON] et al., 2021 [18] & 0.9846 \\ \hline [PERSON] et al., 2021 [19] & 0.9855 \\ \hline [PERSON] et al., 2021 [21] & 0.9835 \\ \hline [PERSON] et al., 2021 [22] & 0.9932 \\ \hline [PERSON] et al., 2022 [23] & 0.9644 \\ \hline Proposed Method & 0.9951 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of Robustness for SPN Attack with recent methods by NC. we will extend our method for blind image watermarking. Secure authentication in the context of the Internet of Things (IoT) is a current open research issue [38]. Moreover, image watermarking using a generative adversarial network provides more robustness against noise interference compared to the existing methods [39].