# HSB-SPAM: An Efficient Image Filtering Detection Technique
## 5 Conclusions  
In this paper, a filtering detection method based on HSB-SPAM has been proposed without any prior information on the modification or forgery. The performance of the proposed detector was verified to detect three types of filtering operations--Median, mean and Gaussian filtering. The proposed method gave higher accuracy than the conventional detectors, especially on JPEG compressed images. The main reason behind the higher accuracy was the use of higher significant bit-plane for difference arrays, where more effective feature set could be constructed by the proposed method. In addition, the proposed method considered the multiple difference arrays for stronger feature set. As a result, the proposed detector with the Markov chain could achieve a higher accuracy than the existing filtering detection techniques in the most cases of the scenarios. Further, the proposed method was also superior in terms of cross-analysis. In the future, the proposed method will be examined for some other types of image operators that can provide promising results in many scenarios and robust techniques can also be proposed that can detect modifications of the image. Both authors discussed the contents of the manuscript and contributed equally to its preparation. [PERSON] designed and implemented the proposed technique. [PERSON]-[PERSON] designed the manuscript and conducted the literature review. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline JPEG & \multicolumn{3}{c}{**Training: ORI-MF3 \& Testing: ORI-MF5**} & \multicolumn{3}{c}{**Training: ORI-MF5 \& Testing: ORI-MF3**} \\ \hline  & GLF & GDCTF & PERB & SPAM & \begin{tabular}{c} HSB- \\ SPAM \\ \end{tabular} & GLF & GDCTF & PERB & SPAM & \begin{tabular}{c} HSB- \\ SPAM \\ \end{tabular} \\ \hline NC & 0.97 & 17.49 & 10.76 & 1.08 & 0.07 & 15.84 & 22.87 & 20.59 & 0.11 & 1.38 \\ \hline Q = 70 & 9.68 & 21.00 & 14.24 & 8.33 & 5.83 & 34.68 & 24.81 & 26.05 & 25.69 & 19.02 \\ \hline Q = 50 & 12.37 & 21.94 & 14.84 & 11.73 & 7.92 & 35.46 & 27.80 & 28.21 & 27.22 & 26.42 \\ \hline Q = 30 & 13.64 & 23.36 & 15.96 & 12.89 & 10.20 & 34.79 & 30.04 & 29.37 & 30.23 & **28.55** \\ \hline \hline \multicolumn{10}{c}{Training: ORI-AVG3 \& Testing: ORI-AVG5} & \multicolumn{3}{c}{Training: ORI-AVG5 \& Testing: ORI-AVG3} \\ \hline  & GLF & GDCTF & PERB & SPAM & \begin{tabular}{c} HSB- \\ SPAM \\ \end{tabular} & GLF & GDCTF & PERB & SPAM & \begin{tabular}{c} HSB- \\ SPAM \\ \end{tabular} \\ \hline NC & 11.51 & 8.41 & 8.26 & 1.27 & 3.14 & 18.54 & 15.88 & 16.89 & 16.48 & 17.00 \\ \hline Q = 70 & 19.02 & 10.16 & 9.30 & 5.31 & 4.07 & 19.99 & 19.21 & 18.87 & 29.93 & 16.93 \\ \hline Q = 50 & 11.29 & 11.10 & 9.90 & 5.49 & 5.08 & 20.18 & 20.37 & 20.59 & 32.10 & 18.31 \\ \hline Q = 30 & 8.63 & 12.71 & 10.01 & 6.32 & 4.97 & 20.18 & 21.00 & 21.79 & 36.25 & 21.11 \\ \hline \hline \multicolumn{10}{c}{Training: ORI-GAU3 \& Testing: ORI-GAU5} & \multicolumn{3}{c}{Training: ORI-GAU5 \& Testing: ORI-GAU3} \\ \hline  & GLF & GDCTF & PERB & SPAM & \
\begin{tabular}{c} HSB- \\ SPAM \\ \end{tabular} & GLF & GDCTF & PERB & SPAM &
\begin{tabular}{c} HSB- \\ SPAM \\ \end{tabular} \\ \hline NC & 1.08 & 9.12 & 7.62 & 0.89 & 0.71 & 1.61 & 14.20 & 9.94 & 1.42 & 1.27 \\ \hline Q = 70 & 3.59 & 11.62 & 9.94 & 3.96 & 3.33 & 6.09 & 16.59 & 11.40 & 8.26 & 6.24 \\ \hline Q = 50 & 5.23 & 13.23 & 10.05 & 5.57 & 3.92 & 7.92 & 18.12 & 11.70 & 10.13 & 7.88 \\ \hline Q = 30 & 6.13 & 15.10 & 11.81 & 7.21 & 4.56 & 10.46 & 21.00 & 12.97 & 13.15 & 8.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Percentage of detection error for different filter size on training and testing sets. **Funding:** This research was supported by Brain Pool program funded by the Ministry of Science and ICT through the National Research Foundation of Korea (2019H1D3A1A01101687) and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1A09081842).