# HSB-SPAM: An Efficient Image Filtering Detection Technique
## 4 Experimental Results
### Results for Non-Filtered and Filtered Images  
In this section, results are displayed for non-filter vs. filtered images on different image size and compression quality. In Table 1, the percentage of detection error for ORI vs. MF3 and ORI vs. MF5 images is provided. The results of the proposed method (HSB-SPAM) are outstanding in the most of the cases as there is significant improvement on low resolution and compressed images. Best results of each case are highlighted by the shaded background in the tables. The detection of the 3 \(\times\) 3 filter size median filtered images is challenging than the 5 \(\times\) 5 filter size median filtered images. The performance of PERB and GDCTF methods are comparatively low. It shows that many existing techniques performance is good in the case of zero padded median filtered images only. Especially, the zero padding increases the occurrence of zero's in the filtered image in small size images (Figure 7a). The higher number of zero's helps the classifier to classify the images easily. To overcome the zero artifacts, the first filtering is applied on the large size image and then the required image size is cropped from the center region. In Table 2, experimental results are shown for detection of non-filtered and mean filtered images, ORI vs. AVG3 and ORI vs. AVG5. The results are given for image size 128 \(\times\) 128, 64 \(\times\) 64 and 32 \(\times\) 32. The different image set are constructed using post JPEG compression of quality Q = [70,50,30]. The detection of mean filtered images of 3 \(\times\) 3 filter size is more challenging than the 5 \(\times\) 5 filter size. The performance of the proposed method is better than GLF technique. The performance of GDCTF, PERB and SPAM is inferior in the most of the cases. Actually, the detection of median filtered is more challenging than any other filter because median filter is a non-linear filter. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**IMAGE** SIZE} & \multirow{2}{*}{**JPEG**} & \multicolumn{8}{c}{**ORI vs. MF3**} & \multicolumn{8}{c}{**ORI vs. MF5**} \\ \cline{3-11}  & & \multirow{2}{*}{**COM.**} & & \multirow{2}{*}{**GLF**} & & \multirow{2}{*}{**GDCTF**} & & \multirow{2}{*}{**PERB**} & & \multirow{2}{*}{**SPAM**} & & \multirow{2}{*}{**HSB-SPAM**} & & \\ \cline{3-11}  & & & & & & & & & & & & \\ \hline \multirow{8}{*}{\(128\times 128\)} & No & 0.38 & 12.67 & 6.32 & 0.00 & 0.38 & 0.76 & 8.04 & 5.28 & 0.00 & 0.83 \\ \cline{2-11}  & Q = 70 & 6.56 & 16.08 & 9.10 & 7.08 & 3.40 & 2.74 & 9.92 & 6.53 & 3.02 & 2.33 \\ \cline{2-11}  & Q = 50 & 9.51 & 17.26 & 10.00 & 11.91 & 4.79 & 3.51 & 10.30 & 6.91 & 4.90 & 3.65 \\ \cline{2-11}  & Q = 30 & 12.08 & 19.41 & 11.46 & 15.80 & 7.19 & 4.79 & 10.81 & 7.60 & 7.53 & 4.38 \\ \hline \multirow{8}{*}{\(64\times 64\)} & No & 0.59 & 17.49 & 9.41 & 0.17 & 0.83 & 1.35 & 10.24 & 7.99 & 0.17 & 1.39 \\ \cline{2-11}  & Q = 70 & 9.41 & 20.90 & 14.48 & 12.05 & 6.42 & 4.93 & 11.87 & 10.07 & 6.67 & 4.59 \\ \cline{2-11}  & Q = 50 & 13.30 & 21.97 & 14.79 & 17.19 & 8.68 & 6.39 & 13.29 & 10.63 & 10.24 & 5.94 \\ \cline{2-11}  & Q = 30 & 15.83 & 23.47 & 16.22 & 21.84 & 11.49 & 7.83 & 14.72 & 11.04 & 12.15 & 6.98 \\ \hline \multirow{8}{*}{\(32\times 32\)} & No & 1.01 & 23.68 & 13.68 & 0.28 & 1.35 & 1.77 & 18.53 & 11.01 & 0.42 & 2.12 \\ \cline{2-11}  & Q = 70 & 14.93 & 25.66 & 19.97 & 19.51 & 10.73 & 7.92 & 20.13 & 13.75 & 11.46 & 8.40 \\ \cline{1-1} \cline{2-11}  & Q = 50 & 18.54 & 27.91 & 22.26 & 23.96 & 15.73 & 10.12 & 21.21 & 14.58 & 13.09 & 9.86 \\ \cline{1-1} \cline{2-11}  & Q = 30 & 21.98 & 29.09 & 23.65 & 28.58 & 19.41 & 11.91 & 22.11 & 15.45 & 17.67 & 11.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Percentage of detection error onMF3 and MF5 images. The Gaussian filter is also used to hide the traces of forgery. From analysis of experimental results provided in Table 3, the detection of Gaussian filtered images is more challenging than the mean filtered images though less challenging than median filtered images.