# Children's Safety on YouTube: A Systematic Review
## 4 Approaches and Existing Methods
### Video Frame Analysis Approach
The CNN-based technique selected crucial frames that exhibited abrupt or gradual changes based on the visual similarity of the adjacent frames in the video. Frame similarity was determined by utilizing the descriptive effectiveness of the Hue, Saturation, and Value (HSV) and Speeded-Up Robust Features (SURF) histogram descriptors. An accuracy of 74% was achieved with CNN based approach, and with supervised classification, they obtained an accuracy of 84%. Further, to identify the community of safe and unsafe content promoters, a network-based approach was applied. [PERSON] et al. [76] designed and implemented an Explicit Content Detection (ECD) system Safe for Work (NSFW) or not suitable media image or video content. The strategy was built on methods for image processing, skin tone detection, and pattern recognition. The image was first transformed using the YCbCr color scheme to identify various objects that were not of relevance. Second, the image's skin tone detection threshold was calculated to filter out different image segments. To identify whether or not an image contains pornographic content, the image explicitness probability was computed. The images were sent to the ECD-CNN model, which was implemented using the CafeNet deep learning framework [77], for classification into explicit and non-explicit images. A multi-modal deep learning classifier was developed by [PERSON] et al. [78] to detect inappropriate cartoon videos for kids. For image analysis, they used the output from VGGNet, and for text analysis, they used the output from LSTM. The VGGNet network is used to identify cartoon image characters, and the LSTM network is utilized to analyze user comments and closed captions related to a video. The VGGNet model was trained and tested on a manually annotated image dataset of 6000 cartoon characters, while the LSTM model was trained and evaluated on a dataset consisting of around 290,000 labeled text records. The LSTM network had a testing accuracy of 94%, whereas the VGGNet network had a testing accuracy of 99%. [PERSON] et al. [21] created a deep-learning architecture to detect inappropriate video platform content. Their dataset includes over 1000 videos with violent, fake, or explicit content. They used frame histograms to represent visual features of video frames and cosine-distance between successive frames to detect changes in scenes when processing data. They used a VGG19 pre-trained CNN that had been trained on ImageNet to extract features. The classification model was fed with three different types of features: frame features, movement features, and audio features. Frame features were extracted by running them through a pre-trained CNN, and they noticed that fake videos looked different than real ones. For movement features, the difference in movement characteristics between fake and real cartoon characters was quite noticeable. The characters in a fake cartoon barely move their limbs. Their motion was jerky and hard, so features related to character movement are critical for the CNN model. To extract audio features, each frame's audio spectrogram was passed through the CNN, and its output was recorded. Almost all of the fake and explicit videos had little or no audio. All three--audio, movement, and frame features--were fed to the VGG19 CNN and LSTM models for classification into violent, fake, or inappropriate video content. They achieved an accuracy of more than 90%. [PERSON] et al. [79] presented a multi-modal (audio and image features) architecture based on Convolutional Neural Networks (CNNs) for detecting inappropriate scenes in video files. Image features were extracted using InceptionV3 as the visual backbone, and audio features were extracted using VGG as the audio backbone. Image features were scaled to a size of 1024 by PCA, and audio was scaled to 128 sizes by PCA. Finally, the image and audio embeddings were concatenated to form the final video embeddings with 1152 dimensions. The video embeddings were then fed into a Support Vector Machine for classification.