# Image Forensics Using Non-Reducing Convolutional Neural Network for Consecutive Dual Operators
## 4 Experimental Results
### Comparative Analysis  
The proposed CNN can classify two operators and their sequence in low resolution, compressed, and uncompressed images. The detailed experimental analysis is discussed above. Here, the results of the proposed scheme are compared with some other state-of-the-art techniques. In the CNN model [13], a constrained convolutional layer is introduced, unlike other traditional models. Different size filters, 7 \(\times\) 7, 5 \(\times\) 5, and 3 \(\times\) 3 are used in the convolutional layer. In our experimental analysis, small-size filters are more suitable for detecting an image processing operation as shown in Figure 13. [PERSON] and [PERSON] [25] applied a modified constrained convolution layer on image residual for  
to a lower number of convolutional layers and large-size filters. [PERSON] et al. [26] proposed the two-stream CNN model. The results of the two-stream model are impressive. The idea of detection of operator sequence is also a milestone in the research. The two-stream model can detect known as operators with unknown specifications. The computational cost is very high due to a large number of layers and customized preprocessing that need to be applied for the detection of different operators and compressed images. Our proposed network is moderate in size and requires less computation due to the bottleneck approach. The bottleneck approach can reduce the learning parameters and allow for increasing the network depth. Results are compared in Figure 13 for multiple scenarios. Both uncompressed and compressed types of images are considered in the comparison. The operators \(\alpha\), \(\beta\), and compression status are shown in the first, second, and third row in Figure 13.  
Further, a detailed comparative analysis is performed with the method [26] because it is comparable with the proposed method. The other compared methods [13; 25] performance is quite low as displayed in Figure 13. In Table 6, the average classification error of five class classifications is presented. The performance of [PERSON] et al. [26] is inferior to our proposed architecture due to several reasons. Primarily, [PERSON]'s CNN contains multiple pooling layers that lose vital statistical information. Large size kernels have also reduced the performance. In the proposed CNN, a non-reducing approach is followed to retain inherited fingerprints as many as possible. The minimum reduction in classification error is for \(\alpha\) = MF5 & \(\beta\) = UP_1.5, i.e., 2.25% and the highest reduction in classification error is for \(\alpha\) = SH_2.0 & \(\beta\) = UP_1.2, i.e., 9.22% of the proposed scheme. Therefore, there is substantial improvement in detection performance.  
In Table 7, a comparative analysis is given for JPEG compressed images. For uncompressed images, the performance of the proposed CNN is already found superior to [PERSON]'s CNN. Multiple compression factors are considered for unbiased analysis. The bottleneck approach allows fourteen convolution layers without the extra burden on computational cost. The classification error of [PERSON] et al. [26] is less than the proposed scheme only in one case, i.e., \(\alpha\) = GAU_1.0 & \(\beta\) = UP_1.5. Otherwise, the proposed CNN outperforms with a good score. The average classification error of the proposed method is less and exclusive of specific preprocessing.  
Figure 13: Comparative analysis in multiple scenarios.