# HSB-SPAM: An Efficient Image Filtering Detection Technique
## 4 Experimental Results
### Results for Complex Scenarios  
In this section, two complex scenarios are considered using UCID dataset. In Table 5, the comparative results analysis between filtered images is provided. The provided results represent the percentage detection accuracy for the image set of size 64 \(\times\) 64 pixels. The image set contains uncompressed and post JPEG compressed image with quality Q = [70,50,30]. The accuracy between median filtered images and other filtered images are greater than 95% in uncompressed image set. The detection between filtered image set pairs, AVG3 vs. GAU3, AVG3 vs. GAU5 and GAU3 vs. GAU5 are affected by post JPEG compression and detection accuracy sharply decreased. In the case of JPEG compressed images, HSB-SPAM method outperforms the existing methods such as SPAM, GLF, GDCTF and PERB whereas the performance of all the methods including HSB-SPAM, SPAM and GLF is quite promising in the case of uncompressed images. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **Technique** & **NC** & **Q = 70** & **Q = 50** & **Q = 30** & **Technique** & **NC** & **Q = 70** & **Q = 50** & **Q = 30** \\ \hline \multirow{4}{*}{\begin{tabular}{c} ORI \\ vs. \\ \end{tabular} } & GLF & 0.63 & 10.35 & 13.43 & 17.26 & GLF & 1.39 & 4.93 & 6.71 & 7.99 \\ \cline{2-10}  & GDCTF & 19.24 & 22.78 & 24.17 & 23.93 & GDCTF & 11.06 & 12.11 & 13.56 & 15.16 \\ \cline{2-10}  & PERB & 9.60 & 15.49 & 16.27 & 17.84 & MF5 & PERB & 8.39 & 10.07 & 11.05 & 11.59 \\ \cline{2-10}  & SPAM & 0.19 & 13.01 & 17.36 & 22.71 & SPAM & 1.19 & 7.13 & 11.06 & 12.52 \\ \cline{2-10}  & HSB-SPAM & 0.88 & 6.49 & 9.46 & 11.61 & HSB-SPAM & 0.47 & 4.64 & 6.53 & 7.61 \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} ORI \\ vs. \\ \end{tabular} } & GLF & 1.43 & 3.90 & 5.03 & 6.96 & GLF & 1.45 & 2.92 & 3.51 & 3.63 \\ \cline{2-10}  & GDCTF & 8.87 & 9.19 & 10.92 & 13.01 & GDCTF & 6.55 & 7.47 & 8.34 & 9.51 \\ \cline{2-10}  & PERB & 9.10 & 10.88 & 11.01 & 12.00 & OSI & PERB & 5.84 & 8.02 & 8.44 & 7.99 \\ \cline{2-10}  & SPAM & 2.04 & 6.07 & 8.39 & 10.14 & SVAG5 & SPAM & 1.59 & 4.20 & 5.49 & 5.58 \\ \cline{2-10}  & HSB-SPAM & 1.57 & 3.37 & 4.76 & 5.90 & HSB-SPAM & 0.76 & 1.97 & 2.30 & 3.00 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} ORI \\ vs. \\ GAU3 \\ \end{tabular} } & GLF & 1.64 & 4.75 & 6.92 & 8.93 & GLF & 1.33 & 3.33 & 4.53 & 5.61 \\ \cline{2-10}  & GDCTF & 9.43 & 11.69 & 14.30 & 16.36 & GDCTF & 6.41 & 7.27 & 8.46 & 9.87 \\ \cline{2-10}  & PERB & 7.83 & 11.11 & 11.26 & 12.85 & OSI & PERB & 6.20 & 8.17 & 9.09 & 9.61 \\ \cline{2-10}  & SPAM & 0.71 & 7.12 & 9.71 & 13.32 & GAU5 & SPAM & 0.79 & 4.20 & 6.32 & 7.47 \\ \cline{2-10}  & HSB-SPAM & 1.56 & 3.62 & 5.66 & 7.62 & HSB-SPAM & 1.18 & 3.44 & 4.12 & 4.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Percentage of detection error on 64 \(\times\) 64 filtered images. In Table 6, the different filter size is used in training and testing set. For example, if ORI and MF3 pair is used in training set, then ORI and MF5 pair is used in testing, where median, mean and Gaussian filtering with filter size 3 \(\times\) 3 and 5 \(\times\) 5 is used. In experimental results, the proposed method are better results for 3 \(\times\) 3 filter size in training pair and 5 \(\times\) 5 in testing pair in comparison to 5 \(\times\) 5 filter size in training pair and 3 \(\times\) 3 in testing pair. In most of the cases, the detection error of the proposed method is less than other methods.