Hybrid Deep Learning Model Based on GAN and RESNET for Detecting Fake Faces

Abstract

While human brains have the ability to distinguish face characteristics, the use of advanced technology and artificial intelligence blurs the difference between actual and modified images. The evolution of digital editing applications has led to the fabrication of very lifelike false faces, making it harder for humans to discriminate between real and made ones. Because of this, techniques like deep learning are being used increasingly to distinguish between real and artificial faces,producing more consistent and accurate results. In order to detect fraudulent faces, This paper introduces a pioneering hybrid deep learning model, which merges the capabilities of Generative Adversarial Networks (GANs) and the Residual Neural Network (RESNET) architecture, aimed at detecting fake faces. By integrating GANs' generative strength with RESNET's discriminative abilities, the proposed model offers a novel approach to discerning real from artificial faces. Through a comparative analysis, the performance of the hybrid model is evaluated against established pre-trained models such as VGG16 and RESNET 50. Results demonstrate the superior effectiveness of the hybrid model in accurately detecting fake faces, marking a notable advancement in facial image recognition and authentication. The findings on a benchmark dataset show that the proposed model obtains outstanding performance measures, including precision 0.79, recall 0.88, F1-score 0.83, accuracy 0.83, and ROC AUC Score 0.825. The study's conclusions highlight the hybrid model's strong performance in identifying fake faces, especially when it comes to accuracy, precision, and memory economy. By combining the generative capacity of GANs with the discriminative capabilities of RESNET, this solves the problems caused by more complex fake face generation approaches.With significant potential for use in identity verification, social media content moderation, cybersecurity, and other areas, the study seeks to advance the field of false face identification. In these situations, being able to accurately discriminate between real and altered faces is crucial. Notably, our suggested model adds Channel-Wise Attention Mechanisms to RESNET50 at the feature extraction phase, which increases its effectiveness and boosts its overall performance.