Object tracking and detection techniques under GANN threats: A systemic review

Abstract

Current developments in object tracking and detection techniques have directed remarkable improvements in distinguishing attacks and adversaries. Nevertheless, adversarial attacks, intrusions, and manipulation of images/ videos threaten video surveillance systems and other object-tracking applications. Generative adversarial neural networks (GANNs) are widely used image processing and object detection techniques because of their flexibility in processing large datasets in real-time. GANN training ensures a tamper-proof system, but the plausibility of attacks persists. Therefore, reviewing object tracking and detection techniques under GANN threats is necessary to reveal the challenges and benefits of efficient defence methods against these attacks. This paper aims to systematically review object tracking and detection techniques under threats to GANN-based applications. The selected studies were based on different factors, such as the year of publication, the method implemented in the article, the reliability of the chosen algorithms, and dataset size. Each study is summarised by assigning it to one of the two predefined tasks: applying a GANN or using traditional machine learning (ML) techniques. First, the paper discusses traditional applied techniques in this field. Second, it addresses the challenges and benefits of object detection and tracking. Finally, different existing GANN architectures are covered to justify the need for tamper-proof object tracking systems that can process efficiently in a real-time environment.