Wireless Multimedia Sensor Networks (WMSNs) nowadays are often used in multimedia applications like video streaming, environmental monitoring, and surveillance in real time. The use of these networks results in the superior transmission of audio, video and other types of data which enhances the efficiency of the whole sector. Nevertheless, their vulnerability to various security threats including black hole attacks is a major problem. These attacks occur when a rogue node intercepts and disposes of data packets, which results in a major disruption in the proper function of the network. This research investigates a variety of detection and prevention means for black hole attacks, including conventional methods and new trust-based mechanisms. Besides, the study examines the employing of new deep learning approaches for strengthening the network security by detecting the abnormal patterns which might be construed as an attack. The results have outlined the necessity for strong safety measures to be adopted not only for the maintenance of data integrity but also for protecting.