SynthSecureNet: An Improved Deep Learning Architecture with Application to Intelligent Violence Detection

We present a new deep learning architecture, named SynthSecureNet, which hybridizes two popular architectures: MobileNetV2 and ResNetV2. The latter have been shown to be promising in violence detection. The aim of our architecture is to harness the combined strengths of the two known methods for improved accuracy. First, we leverage the pre-trained weights of MobileNetV2 and ResNet50V2 to initialize the network. Next, we fine-tune the network by training it on a dataset of labeled surveillance videos, with a focus on optimizing the fusion process between the two architectures. Experimental results demonstrate a significant improvement in accuracy compared with individual models. MobileNetV2 achieves an accuracy of 90%, while ResNet50V2 achieves a 94% accuracy in violence detection tasks. SynthSecureNet achieves an accuracy of 99.22%, surpassing the performance of individual models. The integration of MobileNetV2 and ResNet50V2 in SynthSecureNet offers a comprehensive solution that addresses the limitations of the existing architectures, paving the way for more effective surveillance and crime prevention strategies.