Unveiling Spoofing Attempts: A DCGAN-based Approach to Enhance Face Spoof Detection in Biometric Authentication

Abstract

Face spoofing attacks have become more dangerous as biometric identification has become more widely used. Through the utilisation of false facial photographs, attackers seek to fool systems in these assaults, endangering the security of biometric authentication devices and perhaps allowing unauthorized access to private information. Effectively recognizing and thwarting such spoofing attacks is critical to the dependability and credibility of biometric identification systems in a variety of applications. This research seeks to offer a unique strategy that uses Deep Convolutional Generative Adversarial Networks (DCGANs) to improve face spoof detection in order to counter the challenge provided by face spoofing assaults. In order to strengthen the security of biometric authentication systems in applications like identity verification, access control, and mobile device unlocking, the goal is to increase the accuracy and effectiveness of facial spoof detection. The training dataset is then supplemented with these artificial images, which strengthens the face spoof detection system's resilience. More accurate face spoofing is made possible by the strategy that leverages the discriminative characteristics obtained throughout the process to train the discriminator network employing adversarial learning to discriminate between actual and fake images. Experiments on the CelebFacesAttributes (CelebA) datasets show how effective the suggested method is over traditional techniques. The suggested technique outperforms conventional methods and achieves an astounding accuracy of 99.1% in face-spoof detection systems. The system exhibits impressive precision in differentiating between real and fake faces through the efficient use of artificial intelligence and adversarial learning. This effectively decreases the possibility of unwanted access and enhances the overall dependability of biometric authentication methods.