## 1 Introduction  
The use of closed-circuit television (CCTV) systems is of critical importance in modern society for the purpose of ensuring public safety and security [1, 2]. Surveillance systems function as a preventive measure [3] against unlawful activities and improper conduct by means of ongoing monitoring in residential areas, public spaces, transportation hubs, and transportation hubs [4, 5, 6]. In addition to preventing crime, surveillance technologies also help in quickly identifying and capturing people involved in illegal activities [7]. This enhances the capacity of law enforcement and allows for timely reactions to emerging threats. Furthermore, surveillance data are a crucial kind of forensic evidence in legal procedures, assisting in determining the guilt of criminals and guaranteeing justice for crime victims [8, 9]. Surveillance is a fundamental aspect of proactive security measures, enhancing society's resilience against many possible threats and vulnerabilities [10, 11]. Violence detection is one subfield of surveillance that is extremely important to society because it provides an early warning system for identifying and reducing physical violence, conflict, and injury [12, 13]. Violence detection systems are able to automatically identify violent incidents on CCTV footage and report them for immediate intervention by utilizing advanced deep learning techniques, such as the proposed SynthSecureNet architecture. The proposed architecture can be integrated with violence systems that do not only allow law enforcement and security personnel to respond quickly and improve public safety, but also give citizens a sense of security and assurance. Furthermore, assisting law enforcement in paying attention to tense situations before they go out of control will reduce chances of people/communities getting harmed, and also violence detection technologies are able to stop conflicts from getting worse. The proactive approach to maintaining peace and order in society that is showcased by the integration of violence detection systems into a surveillance infrastructure highlights the innovative influence of technology on public safety initiatives [14; 15; 16]. As a result, in this paper, a hybrid deep learning architecture is proposed with enhanced performance when compared with other deep learning architectures for violence detection. \"SynthSecureNet\" is a unique name for the proposed ensemble architecture. The term \"Synth\" suggests synthesis or combining, and \"SecureNet\" implies a focus on security. This name effectively communicates that the proposed model is a synthesized ensemble designed for security-related tasks. [PERSON] et al. [17] proposed a bagging framework known as IBaggedFCNet. This framework applies ensembles for classification and anomaly detection for motion pictures or videos. The authors used a complex Inception-v3 image classification network in their technique and did not use segmentation prior to extracting features from the video. This eliminated the chance for segmentation results from cross interference and reduced the memory usage. They demonstrated significant improvements on different benchmark datasets, with the best improvements recorded when using the UCF-Crime dataset. They conducted several experiments on several ensemble fusion techniques, such as static and dynamic fusions [17; 18; 19]. The SynthSecureNet architecture stands out from other ensemble architectures based on the combination of the ResNet50V2 and MobileNetV2 architectures. Previous studies have primarily dealt with integrating models that follow similar structural frameworks or using temporal-spatial data separately; on the other hand, SynthSecureNet combines the deep feature extraction strength of ResNet50V2 with the processing speed and light weight of MobileNetV2. This integration also helps to strengthen the model's ability to recognize potential signs of violence and increases the scope of implementing the system in real time with minimal computational power [20; 21; 22].