الفهرس 
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Abstract
The emergence of screened films has led to an increased need for video content classification. However, a significant source of violence in these films can have a negative psychological impact on teenagers. Also, It’s important to filter sensitive content such
as pornography and violence due to the increasing consumption of films by people of all ages. The use of deep learning in computer vision has shown great success and is currently
receiving a lot of attention from researchers in this field. Inappropriate video content filtering has become a significant problem in modern society, particularly as internet access
becomes more widely available. With the advancements in machine learning and neural
network technology, many researchers have focused on creating models that can filter out
pornography and other inappropriate content in movies. However, these models may not
be as effective when it comes to filtering out inappropriate content in cartoons directed at children, as the filtering criteria for these types of videos are different from that for adult
content.
In this thesis, we propose a new CNN model called InspectorNet. InspectorNet is a deep
neural network model designed to effectively detect and filter out inappropriate content
such as pornography and violence in videos. This model utilizes both convolutional neural networks and transformer neural networks to overcome the limitations of current artificial
neural network models. This research compares the performance of InspectorNet against ResNet and other previous methods used in this field, using a well-known animated cartoon
images dataset called Danbooru2018 with a different number of classes. The result shows
that InspectorNet outperforms ResNet in terms of classification accuracy. The comparison
highlights that while InspectorNet requires significant computational resources for training,
It shows better classification performance in inappropriate content filtering.