## 3 Experimental Results and Analysis
### Results  
Extensive experiments were conducted to prove the validity, robustness and effectiveness of the proposed image forgery detection and localization approach. The CASIA V 1.0 image forgery dataset was used in our experiments to test the forgery detection and localization capability of the proposed approach. In addition, a group of standard images from the SIPI image dataset of TIFF format of size (512 \(\times\) 512) were also used to compare the performance of the proposed approach with the state-of-the-art approaches. Three groups of experiments were performed, which were named as follows: (1) images with forgery only, (2) images with combined effects (i.e., forgery + another attack), and (3) comparative analysis. Tables 1-7 are presented in the following subsections to show the results from the CASIA V 1.0 and SIPI datasets. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Measure/** & \multicolumn{3}{c}{**Forgery Classification Measures**} & \multicolumn{6}{c}{**Image Quality Measures**} \\ \cline{2-9}
**Category** & **Precision** & **Recall** & **F1 Score** & **NC** & **TAF** & **BER** & **PSNR** & **SSIM** \\ \hline
**Animal** & 0.98 & 0.96 & 0.97 & 0.95 & 0.95 & 0.05 & 13.51 & 0.68 \\
**Architecture** & 0.96 & 0.97 & 0.96 & 0.97 & 0.97 & 0.03 & 14.77 & 0.72 \\
**Plant** & 0.98 & 0.97 & 0.97 & 0.96 & 0.96 & 0.04 & 13.83 & 0.68 \\
**Texture** & 0.99 & 0.96 & 0.98 & 0.96 & 0.96 & 0.04 & 13.60 & 0.68 \\
**Nature** & 0.98 & 0.99 & 0.99 & 0.96 & 0.96 & 0.04 & 14.00 & 0.69 \\
**Character** & 0.94 & 0.99 & 0.96 & 0.97 & 0.97 & 0.03 & 15.10 & 0.72 \\
**Scene** & 0.98 & 0.93 & 0.95 & 0.97 & 0.97 & 0.03 & 14.84 & 0.71 \\
**AVERAGE** & **0.97** & **0.97** & **0.97** & **0.96** & **0.96** & **0.04** & **14.24** & **0.70** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Forgery classification and image quality measure values for the first group of experiments. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Noise Level/**} & \multicolumn{3}{c}{**Forgery Classification Measures**} & \multicolumn{6}{c}{**Image Quality Measures**} \\ \cline{2-9}  & **Precision** & **Recall** & **F1 Score** & **NC** & **TAF** & **BER** & **PSNR** & **SSIM** \\ \hline
**0.01** & 0.98 & 0.97 & 0.98 & 0.95 & 0.95 & 0.05 & 12.84 & 0.65 \\
**0.02** & 0.97 & 0.94 & 0.96 & 0.94 & 0.94 & 0.06 & 12.43 & 0.63 \\
**0.03** & 0.95 & 0.94 & 0.94 & 0.94 & 0.94 & 0.06 & 12.03 & 0.61 \\
**0.04** & 0.94 & 0.94 & 0.94 & 0.93 & 0.93 & 0.07 & 11.74 & 0.60 \\
**0.05** & 0.94 & 0.94 & 0.94 & 0.93 & 0.93 & 0.07 & 11.45 & 0.58 \\
**AVERAGE** & **0.96** & **0.95** & **0.95** & **0.94** & **0.94** & **0.06** & **12.10** & **0.61** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Forgery classification and image quality measure values for the second group of experiments (Scaling attack). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model Model Measure**} & \multicolumn{3}{c}{**Forgery Classification Measures**} & \multicolumn{6}{c}{**Image Quality Measures**} \\ \cline{2-9}  & **Precision** & **Recall** & **F1 Score** & **NC** & **TAF** & **BER** & **PSNR** & **SSIM** \\ \hline
**1.005** & 0.98 & 0.98 & 0.98 & 0.94 & 0.94 & 0.06 & 12.03 & 0.61 \\
**1.010** & 0.95 & 0.98 & 0.96 & 0.92 & 0.92 & 0.08 & 11.30 & 0.58 \\
**1.015** & 0.92 & 0.95 & 0.94 & 0.92 & 0.92 & 0.08 & 10.87 & 0.56 \\
**1.020** & 0.91 & 0.92 & 0.91 & 0.91 & 0.91 & 0.09 & 10.57 & 0.55 \\
**1.025** & 0.91 & 0.92 & 0.91 & 0.91 & 0.91 & 0.09 & 10.33 & 0.55 \\
**AVERAGE** & **0.93** & **0.95** & **0.94** & **0.92** & **0.92** & **0.08** & **11.02** & **0.57** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Forgery classification and image quality measure values for the second group of experiments (Rotation attack).