# HSB-SPAM: An Efficient Image Filtering Detection Technique
## 4 Experimental Results
### Results for Non-Filtered and Filtered Images
Even for small size, 32 \(\times\) 32 pixels and JPEG Q = 30, the proposed method gives more than 88% and 91% detection accuracy for GAU3 and GAU5, respectively. PERB method [8] give slightly better results for large size (128 \(\times\) 128) and uncompressed image set for the Gaussian filter 3 \(\times\) 3. However, the computation cost of PERB is the highest. A large dataset of ten thousand images is created by using UCID, BOWS2 [37], Columbia [38], and RAISE [39] datasets. The images of 64 \(\times\) 64 are created by cropping center part. In Table 6, percentage of detection error is displayed for both 3 \(\times\) 3 & 5 \(\times\) 5 filter of median, mean and Gaussian filtering. The results are given for non-compressed (NC), and compressed images with JPEG Q = {70,50,30}. However, the results are shown the same pattern as shown in Table 1 for small sized dataset. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{8}{c}{**ORI vs. AVG3**} & \multicolumn{8}{c}{**ORI vs. AVG5**} \\ \cline{3-11}
**IMAGE** & **JPEG** & \multirow{2}{*}{**COM.**} & & \multirow{2}{*}{**GLF**} & & \multirow{2}{*}{**GDCTF**} & & \multirow{2}{*}{**PERB**} & & \multirow{2}{*}{**SPAM**} & & \multirow{2}{*}{**HSB-**} & & \multirow{2}{*}{**GLF**} & & \multirow{2}{*}{**GDCTF**} & & \multirow{2}{*}{**PERB**} & & \multirow{2}{*}{**SPAM**} & & \multicolumn{2}{c}{**HSB-**} \\
**SIZE** & & & & & & & & & & & & & & \\ \hline \multirow{8}{*}{128 \(\times\) 128} & No & 0.63 & 5.11 & 5.63 & 0.45 & 0.56 & 0.56 & 2.36 & 3.40 & 0.66 & 0.76 \\ \cline{2-11}  & Q = 70 & 1.46 & 4.35 & 6.11 & 2.92 & 0.90 & 1.15 & 3.99 & 3.85 & 1.39 & 0.76 \\ \cline{2-11}  & Q = 50 & 1.46 & 5.21 & 6.08 & 4.76 & 1.01 & 1.60 & 4.65 & 3.92 & 2.40 & 1.15 \\ \cline{2-11}  & Q = 30 & 1.81 & 6.22 & 6.88 & 6.39 & 0.97 & 1.67 & 4.97 & 3.65 & 2.60 & 1.56 \\ \hline \multirow{8}{*}{64 \(\times\) 64} & No & 1.39 & 8.53 & 8.75 & 1.91 & 1.49 & 1.32 & 6.01 & 5.73 & 1.53 & 0.69 \\ \cline{2-11}  & Q = 70 & 3.72 & 9.19 & 10.07 & 5.73 & 3.33 & 2.92 & 7.33 & 7.57 & 3.92 & 1.91 \\ \cline{2-11}  & Q = 50 & 4.93 & 10.31 & 10.49 & 7.99 & 4.41 & 3.19 & 8.02 & 7.67 & 5.49 & 2.19 \\ \cline{2-11}  & Q = 30 & 6.39 & 12.63 & 11.11 & 10.14 & 5.73 & 3.33 & 8.65 & 7.40 & 5.42 & 2.78 \\ \hline \multirow{8}{*}{32 \(\times\) 32} & No & 1.53 & 15.69 & 12.26 & 2.71 & 2.26 & 2.19 & 14.24 & 9.03 & 2.64 & 1.25 \\ \cline{2-11}  & Q = 70 & 6.15 & 18.53 & 14.62 & 9.79 & 5.18 & 4.76 & 15.73 & 11.22 & 7.12 & 4.10 \\ \cline{1-1} \cline{2-11}  & Q = 50 & 8.85 & 19.36 & 15.66 & 13.26 & 7.79 & 5.90 & 16.60 & 11.28 & 8.51 & 4.79 \\ \cline{1-1} \cline{2-11}  & Q = 30 & 11.18 & 20.03 & 15.97 & 15.69 & 9.83 & 6.18 & 18.06 & 10.73 & 9.83 & 5.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Percentage of detection error on AVG3 and AVG5 images. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{8}{c}{**ORI vs. GAU3**} & \multicolumn{8}{c}{**ORI vs. GAU5**} \\ \cline{3-11}
**IMAGE** & **JPEG** & \multirow{2}{*}{**COM. **} & & \multirow{2}{*}{**GLF**} & & \multirow{2}{*}{**GDCTF**} & & \multirow{2}{*}{**PERB**} & & \multirow{2}{*}{**SPAM**} & & \multirow{2}{*}{**HSB-**} & & \multirow{2}{*}{**GLF**} & & \multirow{2}{*}{**GDCTF**} & & \multirow{2}{*}{**PERB**} & & \multirow{2}{*}{**SPAM**} & & \multicolumn{2}{c}{**HSB-**} \\
**SIZE** & & & & & & & & & & & & & & \\ \hline \multirow{8}{*}{128 \(\times\) 128} & No & 0.73 & 5.99 & 0.45 & 0.35 & 0.63 & 1.01 & 3.65 & 3.19 & 0.45 & 0.87 \\ \cline{2-11}  & Q = 70 & 2.19 & 8.28 & 1.39 & 3.47 & 1.63 & 2.33 & 5.01 & 4.44 & 1.98 & 1.91 \\ \cline{2-11}  & Q = 50 & 2.74 & 9.39 & 1.