# Children's Safety on YouTube: A Systematic Review
## 4 Approaches and Existing Methods
### Video Frame Analysis Approach
Appropriate classes received an F1-score of 98.95%, while inappropriate classes received an F1-score of 98.94%. [PERSON] et al. [80] proposed multi-media content detection techniques for detecting violent content in videos. Initially, key frames were extracted by comparing histogram differences between successive frames. The SIFT feature extraction technique was used to identify unique interest points. The SIFT descriptors were then vectorized using the Gaussian mixture model and Fisher vector encoding. For classification, Spectral Regression-Kernel Discriminant Analysis (SR-KDA) was used. SIFT features encoded with Fisher vector and SR-KDA correctly classified images containing specific violent content, and an accuracy of 97% was obtained. [PERSON] [2] proposed a multi-modal fusion of audio acoustic, audio transcripts, and video frame methods as an additional layer of content filtering for detecting violent scenes in YouTube videos. The thin-slicing theory was used to extract frames from videos, where several one-second slices were selected randomly from the clip and extracted. The use of a one-second slice ensured that content was analyzed both temporally and in real time. As a pre-processing step for feature extraction, the extracted video slices were segmented into image frames and treated as key frames. Furthermore, CNN, BoVW, and TRoF were used to detect violent scenes. Acoustic features such as spectrograms, MFCCs, and energies were fed into deep neural networks, Gaussian mixture models, and ensemble classifiers, with the best classification results used. The audio was automatically transcribed and analyzed for each slice using automatic speech recognition techniques. Table 4 shows investigated research that falls under this approach. Researchers proposed two types of approaches for detecting inappropriate content in videos. For instance, the authors in [2,17,21,71,72,74,75,78,79] attempted to extract key frames from videos for extracting significant features and use them to build machine learning classifiers using deep learning and transfer learning techniques. Transfer learning techniques like NASNet, Efficient Net-BiLSTM, ResNet 50, InceptionV3, and VGGNet were applied and outperformed YOLOv3 CNN, RNN, and LSTM deep learning techniques. The performance of transfer learning techniques was better when compared to traditional machine learning and deep learning techniques in terms of speed and performance because the models used knowledge (features, weights, and so on) from pre-trained models that have already been trained to recognize the features. Pre-trained networks were more efficient than training neural networks from scratch. The second category of video frame analysis is based on computer vision techniques [73,76,80], in which handcrafted features were extracted by combining SIFT feature descriptors and segmentation techniques with traditional machine learning classification algorithms such as Support Vector Machines to detect skin and non-skin regions. It was also discovered in [80] that computer vision techniques combined with CNN and ResNet50 deep learning techniques performed efficiently in detecting inappropriate content in video frames.