# Children's Safety on YouTube: A Systematic Review
## 4 Approaches and Existing Methods
### Video Frame Analysis Approach
A video-based detection system was developed by [PERSON] et al. [72]. It utilized a YOLOv3 CNN with automatic feature extraction and an RNN to extract temporal information included in videos. They explained how their method may be utilized for both video-level labeling and localizing pornographic content inside videos. They outline an effective technique for locating sexual items inside pornographic video segments and explain how the categories of sexual objects found could be used to determine the intensity (or \"harmfulness\") of the pornographic content. [PERSON] and [PERSON] [17] proposed a deep-learning model to detect and classify inappropriate video content. The efficientNet-B7 model based on CNN is utilized to extract video textual descriptions to feed them to a BiLSTM network, which then classifies videos. The authors manually collected a dataset by searching popular video cartoon names through the YouTube API; irrelevant videos were then filtered out, which resulted in 1126 videos. These videos were split into one-second duration clips, and each of them was manually annotated into safe, sexual-nudity, or fantasy violence classes. The total number of annotated video clips was 111,561, including 27,003 clips belonging to the sexual-nudity class and 26,650 clips belonging to the fantasy violence class. The experimental results showed that the proposed Efficient Net-BiLSTM with 128 hidden units performed well, outperforming other variants of models, including Efficient Net-FC, Efficient Net-SVM, Efficient Net-KNN, Efficient Net-Random Forest. The authors performed experiments to investigate the impact of the attention mechanism on the performance of the proposed Efficient Net-BiLSTM. The outcomes of the experiments with attention mechanism showed that the proposed Efficient Net-BiLSTM with 128 hidden units without attention mechanism outperformed Efficient Net-BiLSTM with attention mechanism-based models with hidden units 64, 128, 256, and 512. [PERSON] et al. [73] developed an application based on skin tone detection filters and a pixel-based approach to identifying pornographic videos and images. With the nudity detection algorithm serving as the system's foundation, the video frames were pre-processed, segmented, and categorized to analyze skin-colored pixels in YCbCr space and then classified as non-skin or skin pixels. The percentage of skin pixels relative to frame size is calculated to be part of the mean baseline that determines whether the files are nude and filters them. A total of 1239 multi-media files were collected from the Web, with 253 videos and 986 images. Using the supplied dataset, the application achieved an accuracy of 80.23% and a precision of 90.33%. [PERSON] et al. [74] proposed KidsGUARD, a fine-grained technique for detecting sparsely distributed child-unsafe content. They used VGG16 CNN, which was pre-trained on approximately 1.3 million ImageNet data as the model learned to classify each image into one of 1000 groups. They used VGG16 to extract a feature vector from the frame; further, the encoded video representations are fed into the LSTM classifier to detect sparse child-unsafe video content. To test their method, they created a dataset of 109,835 video clips that were specifically filtered for child-unsafe content. They achieved a high precision of 80% and a recall of 81% and found that deep learning approaches outperformed baseline encodings such as Vector of Locally Aggregated Descriptors (VLAD) and Fisher Vector (FV) methods in detecting child-unsafe video content. [PERSON] et al. [75] suggested KidsTube, another approach for the detection of child-unsafe content and its promoters. They employed two techniques: a deep learning-based Convolutional Neural Network (CNN) algorithm that uses video frames and supervised classification, which makes use of several video-level, comment-level, and user-level features. For video frame analysis, to detect the different images in a video frame, the CNN was implemented.