# Review on Semi-Fragile Watermarking Algorithms for Content Authentication of Digital Images
## 5 Overview on Semi-fragile Watermarking Algorithms
### Recoverable Semi-Fragile Watermarking Algorithms
#### 5.2.3 Other Transform Domains
The generated watermarks include an authentication watermark and a recovery watermark, both of which are independent with each other. Unlike the traditional quantization matrix, the matrix based on genetic programming involves local features of the original image, which is used for compression purposes. In addition, the algorithm can adopt sparse and dense error pixels to restore and define the intensity of tampering. The proposed algorithm can also be adapted to the authentication process of a color image, using the RGB (red, green and blue) channels to generate the image digest. However, for the visual perception of the watermark, this may lead to bad effects. To compare the above-mentioned semi-fragile watermarking algorithms for tamper detection and recovery in more detail, Table 3 compares the basic information of the above algorithms, and Table 4 gives comparisons about the performance of mentioned algorithms, including the types of non-malicious tampering that the algorithms can resist, the QF of the JPEG compression, the PSNR of the watermarked image, the PSNR of the recovered image, and the ability for tamper detection and location. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & **Non-Malicious** & **Qf of** & **PSNR of** & **PSNR of** & **PSNR of** & **The Ability of Tamper Detection** \\  & **Tampering** & **JPEG (\%)** & **Waremarked** & **Waremark** & **Waremark** \\ \hline \multirow{2}{*}{[26]} & JPEG and & & & & \(R_{\text{FP}}=0\). The performance of the \(R_{\text{FN}}\) is also excellent. \\  & brightness adjustment. & & & & \(R_{\text{FP}}=0\). When the number of \\ \hline \multirow{2}{*}{[27]} & JPEG and AWGN & & & & \(R_{\text{FP}}=0\). When the number of \\ noise. & & & & & \(R_{\text{FP}}\) summed sub-blocks is large, the \\  & & & & & \(R_{\text{FN}}\) will be very low. \\ \hline \multirow{2}{*}{[28]} & JPEG & & & \(36.8\)–\(42.6\) & \(32.0\) & \(R_{\text{FP}}\geq 96\%\) \\ \hline \multirow{2}{*}{[29]} & JPEG and AWGN & & & & \\ noise. & & & & & \\ noise. & & & & & \\ \hline \multirow{2}{*}{[30]} & JPEG, resizing, & & & & \(30\).7 & – \\  & noising, and & & & & \\ filtering. & & & & & \\ \hline \multirow{3}{*}{[31]} & Salt and pepper & & & & \\ noise, JPEG, & & & & & \\ Gaussian noise, & & & & & \\ and brightening & & & & & \\ \hline \multirow{3}{*}{[32]} & JPEG and & & & & \\ Gaussian noise. & & & & & \\ \hline \multirow{3}{*}{[33]} & JPEG, filtering, & & & & \\ scaling, cropping, & & & & & \\ deletion, and & & & & & \\ mixed attacks. & & & & & \\ \hline \multirow{3}{*}{[34]} & JPEG and & & & & \\ wavelet & & & & & \\ compression. & & & & & \\ \hline \multirow{3}{*}{[9]} & JPEG and & & & & \\ Gaussian noise. & & & & & \\ \hline \multirow{3}{*}{[35]} & JPEG & & & & \\ \cline{2-5}  & JPEG, filtering, & & & & \\ scaling, cropping, & & & & & \\ deletion, and & & & & \\ mixed attacks. & & & & & \\ \hline \multirow{3}{*}{[34]} & JPEG and & & & & \\ wavelet & & & & & \\ compression. & & & & \\ \hline \multirow{2}{*}{[9]} & JPEG and & & & & \\ Gaussian noise. & & & & \\ \hline \multirow{2}{*}{[35]} & JPEG & & & & \\ \cline{2-5}  & JPEG & & & \\ \hline \end{tabular}
\end{table} 
Table 4: Comparisons of the performance of various recoverable algorithms.