# Image Watermarking Scheme Using LSB and Image Gradient
## 5 Conclusions  
In order to determine the efficiency of a watermarking technique, some important properties should be analyzed. Firstly, the effectiveness denotes whether the watermark signal embedded in the host image is properly detected. The value of normalized correlation shows the correctness of the watermark; if it is close to 1, the watermark signal is correctly detected. Secondly, imperceptibility denotes whether there is any effect on the perceptual transparency of the watermark embedded in the host image. Thirdly, the payload denotes the mass of content embedded in the digital image. Increasing the content facilitates watermark detection. Fourthly, security denotes the ability to resist against attacks. A secure cryptographic key is secure against cryptographic attacks. In this article, an LSB and image gradient-based image watermarking approach was presented. The original image was divided into nonoverlapping blocks, and the gradient of each block was calculated. In this way, the smooth and irregular areas of the image could be identified for watermark embedding. Finally, LSB was used to introduce watermarked bits. This approach functioned in the time domain, providing computational efficiency and high perceptual quality. It showed significant robustness against image processing and geometrical attacks. In the future, the proposed technique will be investigated for resistance against modern attacks, such as the boomerang attack. Moreover, this technique will be extended for color and video watermarking.  
Conceptualization, Z.B.F; data curation, M.A.R.; formal analysis, M.A., F.A., M.M., J.A. and Z.B.F.; investigation, M.S. and M.A.; methodology, Z.B.F.; project administration, M.S. and M.M.; resources, M.A. and M.A.R.; software, M.A.; supervision, M.A.R., M.S. and M.M.; validation, Z.B.F.; visualization, F.A.; writing--original draft, Z.B.F., M.A. and M.A.R.; writing--review and editing, J.A., M.M., M.S. and F.A.; proofreading and writing the paper script in overleaf, M.A.R. and M.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Taif University Researchers Supporting Project number (TURSP-2020/79), Taif University, Taif, Saudi Arabia.  
Not Applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Attacks**} & \multirow{2}{*}{**Strengths**} & \multicolumn{2}{c}{**Normalized**} & \multirow{2}{*}{**Correlation**} \\ \cline{3-3} \cline{5-5}  & & **Suggested Technique** & [13] \\ \hline S\&P & 0.01 & 0.9970 & 0.9032 \\ S\&P & 0.02 & 0.9957 & 0.9055 \\ Gaussian Noise & 0.05 & 0.9875 & 0.9977 \\ Gaussian Noise & 0.10 & 0.9872 & 0.9816 \\ JPEG & 60\% & 0.9916 & 0.7512 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Normalized correlation against image processing attacks.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Attacks**} & \multicolumn{4}{c}{**Normalized Correlation**} \\ \cline{2-5}  & & **Suggested Technique** & [28] & [13] \\ \hline Image Cropping & 25\% & 0.9975 & 0.2827 & 1.0 \\ Median Filter & 3 \(\times\) 3 & 0.9940 & 0.3124 & 0.8848 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Normalized correlation against image geometrical attacks.