# An Enhanced Multimodal Stacking Scheme for Online Pornographic Content Detection
## 5 Discussion  
The main objective of this study is to develop an enhanced multimodal stacking scheme that can be used in real-time streaming environments by reducing the computation time for feature extractions and judging the harmful status of the input content. To accurately detect the harmful contents, the implicative visual and auditory features are extracted by a bi-directional RNN with VGG-16 and by a multilayered dilated convolutional network, respectively. Moreover, three component classifiers are trained, respectively, by using only the implicative visual features (for video classifier), only the implicative auditory features (for audio classifier), and by using both features together (for fusion classifier). Here, to reduce the detection time, we decreased the number of component classifiers to be stacked in the ensemble scheme to three from four as in the previous scheme proposed in [17]. Then, these three component classifiers are stacked in the enhanced ensemble scheme to reduce the false negative errors in a serial order of the fusion classifier, video classifier, and audio classifier for quick online detections. According to the analysis of the experimental results, the performance rates of the proposed scheme are 95.40%, 92.33%, and 4.60% for the true positive rate, accuracy, and false negative rate, respectively. In recent years, many studies have reported of high performance in the harmful content detections using various deep learning approaches [6; 7; 8; 9; 10; 11; 13; 14]. Among the studies, some use video frame image or video clips [7; 8; 9; 10; 11], motion analysis [6], or age prediction from facial images [14] as the visual element of input content to determine the harmfulness. When comparing the performance results of these approaches, the approach of [6] with the accuracy rate of 95.1% and the approach of [7] with the true positive rates of 97.52% are showed better performance than the enhanced multimodal stacking scheme suggested in this study. However, since the techniques used in [6; 7; 8; 9; 10; 11; 13; 14] cannot properly detect the harmful contents based on the acoustic elements, the proposed scheme in this paper, which includes an auditory element detection, can be evaluated as more advanced. In addition, we investigated as many previous studies as possible that utilize both the visual and auditory elements simultaneously for the detection of harmful content as in this study. We confirm that the performance of the proposed method in this study is more superior to the true positive rate of 94.44% for the current state-of-the-art technology in this field [16]. However, because it is difficult to \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **1 st Classifier** & **2 nd Classifier** & **3 rd Classifier** & **4 th Classifier** & **Weighted Average Detection Time** \\ \hline Enhanced stacking & 0.13 & 0.13 (0.26) & 0.04 (0.30) & None & 0.14 \\ \hline Previous stacking [17] & 0.10 & 0.55 (0.65) & 0.50 (1.15) & 0.03 (1.18) & 0.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the detection time (accumulated time) of the component classifiers (sec/content). use the same data for performance comparisons because of the nature of the research field, it is difficult to determine relative superiority by simply comparing the numerical values published in each paper. In order to provide a meaningful performance comparison, the enhanced stacking scheme proposed in this study is compared to the multimodal stacking scheme of the previous study [17] conducted by our team using the same test data set. According to the experiment analysis results, the performances of the enhanced multimodal stacking scheme are analyzed to have the improved true positive rate of 95.40% and the false negative rate of 4.60% than 94.33% and 5.67% of the previous study, respectively. In addition, it is analyzed that the proposed scheme can detect the harmful contents up to 74.58% and an average of 62.16% faster than the previous scheme.