ABSTRACT
Surge of deep fake videos, enabled by advancements in AI, present a grave challenge to the trustworthiness of visual material. To confront this issue, we present a fresh deep fake detection system that relies on facial embeddings and fine-tuned VGG16-based models. Through capturing subtle facial features and rigorous training with meticulously curated datasets, our system contributes in distinguishing authentic content from manipulated ones. Equipped with a robust real-time video processing pipeline, our approach acts as a proactive shield against the proliferation of false information, thereby preserving the credibility of digital media. By amalgamating deep learning and transfer learning methodologies, our system provides a reliable strategy to combat the malevolent use of deep fake technology. Leveraging sophisticated algorithms and evaluation metrics, we aim to mitigate the risks associated with digital manipulation, ultimately upholding the trustworthiness and authenticity of visual content in the intricate landscape of the digital age.
