Published Paper Details:

This project delves into the critical realm of combating deep fakes in online networking platforms by employing advanced deep learning techniques. Leveraging Generative Adversarial Networks (GANs) to simulate deep fake generation and utilizing the feature extraction capabilities of Inception ResNetV2, there search aims to develop a robust deep fake detection model. The proposed model undergoes comprehensive training, evaluation, and fine-tuning, with a focus on countering adversarial techniques employed in sophisticated deep fake generation. The study contributes are liable and effective means of discerning between genuine and synthetic content, offering heightened security for online networking platforms. Furthermore, insights gained into adversarial strategies and practical deployment recommendations provide a comprehensive approach to addressing the rising threat of deep fakes in the digital landscape. The proposed model undergoes comprehensive training, evaluation and fine-tuning, with focus on countering adversarial techniques employed in sophisticated deep fake generation.