ENHANCING SURVEILLANCE SECURITY: USING GENERATIVE ADVERSARIAL NETWORKS AND COMPUTER VISION TO PREVENT IDENTITY SPOOFING IN FACIAL RECOGNITION SYSTEMS

Abstract

Facial recognition systems have become integral to modern security infrastructures, offering a reliable method for identity verification. However, these systems are vulnerable to spoofing attacks, where adversaries use images, videos, or 3D masks to impersonate individuals and bypass authentication. Traditional anti-spoofing methods often fail to detect sophisticated attacks, necessitating the development of more robust solutions. The primary objective of this research was to explore the potential of GANs in generating synthetic data to train advanced facial recognition models and improve their resistance to spoofing. The study aimed to evaluate the effectiveness of integrating GANs with computer vision techniques for detecting and mitigating spoofing attempts in real-time surveillance scenarios. The results indicate that GAN-based models significantly improve the ability of facial recognition systems to identify and reject spoofed identities. By generating adversarial examples, the GANs enhanced the training process, enabling the facial recognition system to learn subtle patterns indicative of spoofing, such as inconsistencies in facial texture or lighting. The integration of computer vision techniques further strengthened the model's performance, allowing it to detect spoofing attacks in various conditions, including different angles, lighting variations, and partial occlusions. The system demonstrated increased accuracy in distinguishing genuine identities from spoofed ones, with improved adaptability to real-world challenges. This study demonstrates that GANs, when combined with computer vision, offer a promising solution to enhance the security of facial recognition systems against identity spoofing. The approach improves the resilience of these systems in real-world applications by detecting more sophisticated spoofing techniques. However, limitations include the need for large, high-quality datasets to train the models effectively and the computational resources required for real-time processing.