## 2 Methodology
### Block Diagram  
In the process of synthesizing the required architecture, transfer learning was performed, which incorporated MobileNetV2 and ResNet50V2 models as feature extractors. Transfer learning is the idea of using features learned in one task to another different but related task, which helps the model to obtain benefits of pre-learned representations, and thus, the training from scratch is not always necessary. Figure 1 illustrates the development process for the proposed architecture from the preprocessing phase up to inferencing, as shown in Figure 1 below. The algorithm used in SynthSecureNet is summarized in the above block diagram. From the input videos in the preprocessing phase, frames are extracted and augmented further with techniques that include flipping randomly, zooming randomly, adjusting the brightness, and rotating randomly to make the training dataset more diverse. During the feature extraction, the preprocessed frames are then analyzed using MobileNetV2 and ResNet50V2 models, respectively, which are used for feature extraction. MobileNetV2 and ResNet50V2 are convolutional neural networks (CNNs) that have been pre-trained on large-scale image databases including ImageNet and are capable of recognizing quality features within Images. To achieve this, the last convolutional layers of both MobileNetV2 and ResNet50V2 architectures are fed with the input frames to generate their feature maps, which preserves more high-level representations. In this last stage of the network (feature fusion phase), MobileNetV2 and ResNet50V2 feature maps are concatenated along with the channel dimension. Temporal concatenation integrates the two models by merging the feature maps along the channel axis, outputting a fused feature map that includes information from the two models. This fusion step enables the subsequent layers to make use of the complementary features being learned by both models. In the Additional Layers phase, as shown in Figure 1 above, the fused feature map is for further processing of getting features and making predictions. These layers typically include a flattened layer that flattens the fused feature map obtained from the 3D convolu  
Figure 1: Block diagram of the SynthSecureNet architecture. tion into a one-dimensional vector. Dense layers are fully connected layers that specifically calculate complex combinations of features that are extracted out of the flattened structure. Dropout layers come into play during the training process, where a certain part of the units is randomly removed in order to reduce overfitting by the use of this technique. In the output layer phase, the final dense layer employs the sigmoid activation function in classifying the inputs as a binary output to signify the presence or the absence of violence in the input frame. During the training phase, the binary cross-entropy function is used as the loss function for the SynthSecureNet model as it calculates the difference between the probability estimates produced by the model and the ground truth. The model is trained using the Adam optimizer because it allows for the learning rate to be updated for each parameter on the basis of its gradient history. As a post-processing of the individual frames of a video during inference, the preprocessed frames are input to the SynthSecureNet model for frame-level classification. To this end, the frame-level predictions are then combined based on methods such as majority voting or averaging to arrive at the final video-level prediction. Furthermore, together with transfer learning, feature fusion, and additional layers, the proposed SynthSecureNet was designed to learn and classify violence in videos. The pre-trained models, when taken as feature extractors, are already powerful, as they employ and build from prior experience with large datasets of images. Incorporating features of multiple models promotes the models' ability to focus on different and complementary aspects.