# Children's Safety on YouTube: A Systematic Review
## 4 Approaches and Existing Methods
### Audio Content Analysis Approach
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author(s)** & **Dataset** & **Method(s)** & **Class** & **Accuracy** \\ \hline [PERSON] [2] & YouTube videos & CNN, BoVW, TRoF & \begin{tabular}{c} hate speech, violence, \\ complex language, sexual \\ references, drug, and alcohol \\ \end{tabular} & N/A \\ \hline [PERSON] et al. [71] & Elsagate videos & NASNet & \begin{tabular}{c} sensitive \\ non-sensitive \\ \end{tabular} & 92\% \\ \hline [PERSON] et al. [72] & Videos & YOLOv3 CNN, RNN & 19 categories & 87\% \\ \hline [PERSON] and [PERSON] [17] & Cartoon videos & Efficient Net-BiLSTM & \begin{tabular}{c} safe \\ unsafe \\ \end{tabular} & 95\% \\ \hline [PERSON] et al. [73] & Videos, Images & YCbCc, Threshold & \begin{tabular}{c} skin \\ non-skin \\ \end{tabular} & 80\% \\ \hline [PERSON] et al. [74] & ImageNet & VGG 16, LSTM & \begin{tabular}{c} safe \\ unsafe \\ \end{tabular} & 81\% \\ \hline [PERSON] et al. [75] & Cartoon videos & CNN & \begin{tabular}{c} safe \\ unsafe \\ \end{tabular} & 74\% \\ \hline [PERSON] et al. [76] & Videos & \begin{tabular}{c} YCbC, ECD-CNN, \\ ResNet 50 \\ \end{tabular} & \begin{tabular}{c} explicit \\ non-explicit \\ \end{tabular} & 95\% \\ \hline [PERSON] et al. [78] & Cartoon images and labeled text & \begin{tabular}{c} VGGNet \\ LSTM \\ \end{tabular} & \begin{tabular}{c} appropriate \\ inappropriate \\ \end{tabular} & 99\% \\ \hline [PERSON] et al. [21] & Cartoon videos & \begin{tabular}{c} VGG19 \\ LSTM \\ \end{tabular} & \begin{tabular}{c} violent, fake, or \\ inappropriate \\ \end{tabular} & 90\% \\ \hline [PERSON] et al. [79] & Videos & InceptionV3, VGG, SVM & \begin{tabular}{c} appropriate \\ inappropriate \\ \end{tabular} & \
\begin{tabular}{c} F1-score \\ 98.95\% \\ 98.94\%. \\ \end{tabular} \\ \hline [PERSON] et al. [80] & Cartoons & SIFT, SR-KDA &
\begin{tabular}{c} violent \\ non-violent \\ \end{tabular} & 97\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative study on video frame analysis. (SC), Spectral Flux (SF) and Mel-Frequency Cepstral Coefficients (MFCC). On the other hand, four visual features were selected, namely Local Binary Patterns (LBP) with 144-dimensional, Color-Naming Histogram (CNH) with 99-dimensional, Color Moments (CM) with 81-dimensions and Histogram of Oriented Gradients (HOG) with 81-dimensions. The experimental results show that deep neural networks reached an accuracy score of less than 60%, indicating that many improvements were needed. [PERSON] et al. [82] proposed a bloody video detection system based on audio-visual feature fusion. The authors utilized ResNet and bidirectional LSTM (bi-LSTM) in order to extract static images and temporal and spatial features. The ResNet-50 model was utilized to detect static image characteristics from the bloody videos. The resultant feature maps were passed into the bi-LSTM model. The process of extracting audio features consisted of passing the original waveform of audio into the CNN model. The output of the trained CNN model grouped the audio data into either bloody or non-bloody classes. The authors then proposed the idea of feature fusion by introducing an audio-visual feature fusion layer and passing them to the sigmoid activation function for classification purposes. Owing to the lack of bloody video dataset availability, the authors collected 50 bloody videos on YouTube and collected non-bloody video samples from the MediaEval 2015 dataset. The conducted experiments showed that the fusion-based model outperformed the audio-based and visual-based classifiers. The fusion-based model attained an accuracy of 95% taking advantage of combining the visual and audio features into the same feature maps. [PERSON] et al. [83] proposed an automated approach to tackle disrespectful content in videos. The input to the proposed system is a video, and the audio was extracted from the given video. The extracted audio was converted into text using speech-to-text techniques in order to find violent and disrespectful words.