# PornNet: A Unified Deep Architecture for Pornographic Video Recognition
## 4 Experiments
### Ablation Studies
#### 4.3.3 Combining DCNet and RANet  
As discussed in the ablation studies above, we fuse the image and audios recognition results for pornographic video recognition. More specifically, we make use of Equation (9) to produce the final decision. In practice, we conduct two groups of experiments. First, A-ResNet-18 is used to produce the audio recognition results while it is combined with  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Networks**} & \multicolumn{3}{c}{**Precision (\%)**} & \multicolumn{3}{c}{**Recall (\%)**} & \multirow{2}{*}{**Acc (\%)**} \\ \cline{2-2} \cline{6-7}  & **P** & **S** & **N** & **P** & **S** & **N** \\ \hline ResNet50 & 85.0 & 90.3 & 93.5 & 90.1 & 85.0 & 92.4 & 89.2 \\ RCNet & 87.5 & 92.3 & 94.2 & 91.6 & 86.3 & 94.0 & 90.6 \\ FCNet & 88.3 & 93.3 & 94.8 & 92.3 & 87.2 & 94.6 & 91.4 \\ A-RCNet & 88.5 & 93.5 & 94.7 & 92.4 & 87.3 & 94.8 & 91.5 \\ DCNet & 90.0 & 95.5 & 95.1 & 93.5 & 88.5 & 95.5 & 92.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different detection-classification networks. P, S, and N denote videos of a porn, sexy, and normal class, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Features**} & \multicolumn{3}{c}{**Precision (\%)**} & \multicolumn{3}{c}{**Recall (\%)**} & \multirow{2}{*}{**Acc (\%)**} \\ \cline{2-2} \cline{6-6}  & **P** & **N** & **P** & **N** \\ \hline MFCC & 82.9 & 76.0 & 73.1 & 85.0 & 79.1 \\ GFCC & 86.2 & 78.1 & 75.6 & 87.9 & 81.8 \\ log-power SIFT Spectrograms & 82.3 & 82.1 & 82.1 & 82.4 & 82.3 \\ Log Mel-Spectrograms & 85.5 & 87.0 & 87.3 & 85.2 & 86.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The performance of our RANet with different features. Mel-frequency cepstral coefficients (MFCC), gammatone frequency cepstral coefficients (GFCC), and short time fourier transform (STFT).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Networks**} & \multicolumn{3}{c}{**Precision (\%)**} & \multicolumn{3}{c}{**Recall (\%)**} & \multirow{2}{*}{**Acc (\%)**} \\ \cline{2-2} \cline{6-6}  & **P** & **N** & **P** & **N** \\ \hline VGG & 80.6 & 82.6 & 83.1 & 80.0 & 81.6 \\ ResNet-18 & 82.5 & 85.0 & 85.6 & 81.9 & 83.8 \\ A-ResNet-18 & 83.8 & 86.1 & 86.6 & 83.3 & 85.0 \\ ResNet-50 & 83.0 & 85.6 & 86.1 & 82.4 & 84.3 \\ RANet & 85.5 & 87.0 & 87.3 & 85.2 & 86.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of different audio-classification networks.  
five different detection-classification networks presented in Table 1. Second, we replace A-ResNet-18 with RANet for performing porn audio recognition. The results are illustrated in Tables 4 and 5 respectively. By comparing Tables 1 and 4, we can observe that A-ResNet-18 obviously increases the performance from 92.5% to 93.1% obtained by DCNet, along with the accuracy gains of 0.9%, 0.7%, 0.7%, and 0.9% achieved by ResNet50, RCNet, FCNet, and A-RCNet respectively. Particularly, significant performance improvement manifests itself into the precision and recall of porn video recognition. Furthermore, combining A-ResNet-18 and DCNet improves the precision from 85.0% to 93.2% and the recall from 90.1% to 95.0%. This sufficiently demonstrates the beneficial effect of RANet in further performance boost. Thus, the best accuracy at 93.4% is achieved by combining DCNet and RANet.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy and copyright issue.  
The authors greatly appreciate all the reviewers for their positive and constructive comments.  
The authors declare no conflict of interest.