## 2 Related Works  
There are several studies on the detection of offensive behavior using artificial intelligence in the literature. Some of them worked on aggressive behavior detection using images, and others were on insult and aggressive expression detection using different methods like natural language processing. [PERSON] et al. (2020), in their study, created an artificial intelligence model that could detect fight scenes from surveillance cameras. The key aim of the research was to determine incidents of fights using the images obtained from cameras for surveillance as fast and precisely as possible. With this motivation, the paper proposes a method that first improves the traditional ways of fight detection with the proposal of Xception, bi-directional LSTM, and the attention layer. In this context, a new dataset was collected in the form of images captured by surveillance cameras from YouTube and shared publicly. Experiments have also been conducted using some existing datasets like Hockey Fight and Peliculas. It is observed in this study that adding an Xception-based CNN model and an attention layer to the Bi-LSTM network increased the success rate in detecting fights. This model was seen to result in higher accuracy rates as compared with existing methods. According to the outcome of the experiments, this reached accuracy rates of 98% for the Hockey Fight dataset and 100% for Peliculas. However, the success rate of this new collected data stood a bit lower on performance in comparison with other used datasets because of a complex diversified dataset. This proved that it is necessary to further develop the model for more diverse situations. The research not only gave a better and speedier solution to fight detection but also presented the scientific community with a new dataset for further research on the subject [9]. The authors conducted a further study on images by [PERSON] et al. 2010. In this paper, the BEHAVE video dataset was presented for the classification of multi-person behavior. Its focus is presenting a video dataset with an innate ground truth that can then classify group behaviors. This dataset consists of approximately 90,000 frames where the people, the groups they interact with, and the behaviors exhibited by these groups have been labeled, such as walking, fighting, gathering, and so on. The dataset is specifically designed to be used in the analysis of specific group behaviors and tries to provide a basis for the performance evaluation of various artificial intelligence algorithms in this context. In this work, artificial intelligence algorithms that are widely used in classifying behaviors expressed by groups during their interaction are proposed: the hidden Markov model. This model has classified some of the classes, such as walking together, fighting, and approaching, each learned by a separate HMM. The experimental results showed that the classification accuracy reached up to 93.67% using the video information acquired in different windows (e.g., from 5 frames to 100 frames). This study not only provides a rich dataset for automatically detecting and classifying group behaviors but also highlights the importance of developing such datasets [10]. Intheir study, [PERSON] et al. (2019) developed a deep learning-based model that can detect violence in videos. It means that the research is basically trying to create a system for the automatic recognition of violent acts in videos that overcomes traditional surveillance systems' limitations. From this motivation, an end-to-end deep neural network model is proposed that incorporates the VGG-16 model as a spatial feature extractor and the LSTM model as a temporal feature extractor. In this paper, a new benchmark dataset is introduced, namely the Real-Life Violence Situations (RLVS) dataset, to enhance the performance of the model and make it compatible with real-world scenarios. This dataset contains 2000 short video clips of people from different genders, races, and age groups, including 1000 violent videos and 1000 non-violent videos.