# HSB-SPAM: An Efficient Image Filtering Detection Technique
## 3 The Proposed Method
### Higher Significant Bit-Plane Analysis  
In order to collect more valuable statistical information, the proposed method transforms the image into a higher significant bit-plane based image by dropping least significant bits of each pixels and considering only the higher significant bits. The resultant image contains more relevant information in particular range that helps in generating more valuable information with difference arrays than the complete image. To study experimentally and analyze the effectiveness of the higher significant bit-plane based image, a normal probability distribution of pixel difference in the UCID database [36] having 1338 images is considered. The graph of the normal probability distribution is shown in Figure 2, where ORI denotes the non-filtered image, ORI_HSB denotes the higher significant bit-plane based image of ORI images. In general, the 8-bit grayscale image is considered by conventional image filtering detection techniques. However, 4-HSB version of images is considered in this paper. Median filtered image with filter size 3 \(\times\) 3 and 5 \(\times\) 5 are denoted as MF3 and MF5 and their corresponding higher significant bit-plane versions are denoted by MF3_HSB and MF5_HSB. In the same way, mean and Gaussian filtered images with filter size 3 \(\times\) 3 & 5 \(\times\) 5 are denoted as AVG3, AVG5, GAU3 & GAU5, respectively. It is evident from the Figure 2 that the higher significant bit-plane filtered images (MF3_HSB, MF5_HSB, AVG3_HSB, GAU3_HSB, GAU5_HSB) contain a most of the information in small interval whereas the information is distributed into a large interval in normal images. Since the Markov chain is applied on thresholded arrays, the higher significant bit-plane images will perform better in the view of thresholding. To further verify the benefit of higher significant bit-plane images, entropy of the first order pixel difference arrays is calculated from the non-filtered and filtered images. The  
Figure 2: Normal distribution of 8-bit and 4-bit images. entropy dissimilarity of pair ORI and MF3 is less in comparison with pair ORI_HSB and MF3_HSB. The high dissimilarity of low-bit filtered and low-bit non-filtered images give better detection capability. The main reason is that difference array of higher significant bit-plane images performs better. Further, the dissimilarity pattern is also followed by MF5, AVG3, AVG5, GAU3 and GAU5 filtered images as shown in Figure 3.  
As discussed above, the Markov chain is applied on thresholded difference arrays to extract statistical information in compact array, the higher significant bit-plane images provides better information for feature sets without increasing the size of feature vector. The validation of performance benefits in the higher significant bit-plane images is illustrated in Figure 4. In the example, a \(5\times 5\) size pixels image patched from 8-bit image is considered as 8-bit image is commonly used for experimental analysis. The horizontal difference array of the image is further thresholded at threshold \(\Gamma\) = 3. To analyze the variation in information, the change array is constructed. The change array shows the change in the element value of column wise. Since the initial three values are \(-3\) in the first row of thresholded array, there is no change and it is represented in the change array as 0. The fourth value in the first row of thresholded array is three. It means that the value is changed from \(-3\) to 3. Therefore, the change is represented by one. The same analysis is performed on 4-HSB image of the same 8-bit image which is considered in the aforementioned case. It can be seen in the thresholded array and change array that shows better variation on 4-HSB image means that the statistical analysis performed on HSB image will demonstrate better filtering detection accuracy. In addition to 4-HSB depth, other bit-depths can be also considered as a base on the image data set characteristics.