Physics Maths Engineering
Object tracking and detection techniques under GANN threats: A systemic review
Saeed Matar Al Jaber,
Asma Patel
Peer Reviewed
Related Subjects
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.
Key Questions
What are the current developments in object tracking and detection techniques concerning adversarial attacks?
Recent advancements in object tracking and detection have led to significant improvements in identifying attacks and adversaries. However, adversarial attacks, intrusions, and image/video manipulations continue to pose threats to video surveillance systems and other object-tracking applications.
How are Generative Adversarial Neural Networks (GANNs) utilized in image processing and object detection?
GANNs are widely used in image processing and object detection due to their flexibility in processing large datasets in real-time. They are employed to enhance the realism of generated images and improve detection accuracy.
What are the challenges associated with GANNs in the context of adversarial attacks?
While GANN training aims to create tamper-proof systems, the possibility of attacks remains. Adversarial attacks can manipulate images or videos, compromising the integrity of object tracking and detection systems.
What is the focus of the systematic review presented in the paper?
The paper systematically reviews object tracking and detection techniques under threats to GANN-based applications. It evaluates selected studies based on factors such as publication year, implemented methods, algorithm reliability, and dataset size.
How are the selected studies categorized in the review?
Each study is summarized by assigning it to one of two predefined tasks: applying a GANN or using traditional machine learning (ML) techniques.
What aspects are discussed regarding traditional techniques in object detection and tracking?
The paper discusses traditional applied techniques in object detection and tracking, addressing their challenges and benefits.
What is the significance of reviewing different existing GANN architectures?
Reviewing various GANN architectures helps justify the need for tamper-proof object tracking systems capable of efficient real-time processing.
