# HSB-SPAM: An Efficient Image Filtering Detection Technique
## 4 Experimental Results  
This section demonstrates the efficacy of the proposed method by carrying out an experimental study. The UCIDv2.0 [36] database (UCID) has been selected to evaluate the performance of the proposed method since the UCID database has been one of the prime databases being used in literature. All experimental results are shown using UCID database in Tables 1-6 excepting Table 4, where multiple databases are used for experimental analysis. UCID contains 1338 images of size 384 \(\times\) 512 and 512 \(\times\) 384 pixels. However, the images of size 256 \(\times\) 256, 128 \(\times\) 128, 64 \(\times\) 64, and 32 \(\times\) 32 pixels are considered for experimental purposes by cropping center part of original images of UCID database images as shown in Figure 7b, which can eliminate the padding artifacts. In particular, zero padding artifacts are more of a serious concern in filtering detection. The performance of the proposed method (HSB-SPAM) is compared with some of the popular and closely related image forensic methods such as GLF [12], GDCTF [30], PERB [17], and SPAM [10].  
The performance is evaluated by an average percentage accuracy on 100 training-testing pairs using two classifiers, namely support vector machines (SVM) and linear discriminant analysis (LDA). The analysis of experimental results is presented in four-subsections defined as follows.  
Both uncompressed and compressed images with JPEG quality Q = [70,50,30] and size of 128 \(\times\) 128, 64 \(\times\) 64, and 32 \(\times\) 32 pixels are used for experimental purposes. The non-filtered images are denoted by ORI. Median filtered images with a filter size of 3 \(\times\) 3 are denoted by MF3. Similarly, mean filtered and Gaussian filtered images of a filter size 3 \(\times\) 3 are denoted by AVG3 and GAU3, respectively. Median, mean and Gaussian filtered images of filter window size 5 \(\times\) 5 are denoted by MF5, AVG5 and GAU5, respectively. The compression is applied after filtering, i.e., post JPEG compression in filtered images. The SVM classifier with linear kernel is taken as a classifier. More than one hundred training testing pairs are formed for unbiased result analysis. Both the training and testing set have an equal number of images. The experimental results are represented in the form of average detection accuracy and average percentage error. For the proposed method, optimum SVM hyper-parameters are achieved using minimum classification error plot as shown in Figure 8. The estimated and observed minimum classification error point denotes the estimated and observed error while considering different hyper parameter values in the process of Bayesian optimization. The best hyper-parameter point denotes the iteration of corresponding optimized hyper-parameters. The minimum error hyper-parameters point defines the iteration of respective hyper parameters that produce the observed minimum classification error. The ORI and MF3 images of image sizes 64 \(\times\) 64 and JPEG Q = 50 are taken for parameter selection. The hyper-parameters are utilized for all other experimental results in rest of the paper.  
Figure 8: Optimization of the SVM hyper-parameters selection.  
Figure 7: Padding and center cropping: (**a**) zero padding; (**b**) center part cropping.  
Experimental results are discussed in two sections. In Section 4.1, results are discussed between non-filtered and filtered images in Tables 1-3 on UCID dataset and in Table 4 for large dataset. In Section 4.2, Table 5 displayed the results where both sets contain only filtered images either of different filter size, different filter or both. In Table 6, different sized filtered images are considered in the training and testing set.