Abstract

The increasing prevalence of DeepFake technology poses significant threats to various industries and public trust, making the development of robust detection methods crucial. In this study, we propose a novel approach for DeepFake detection using the InceptionResNetV2 architecture, leveraging its advanced capabilities in extracting features from images. Our method utilizes a deep learning framework to train the model on a diverse dataset of authentic and DeepFake videos, enabling it to learn distinct patterns and discrepancies between the two types of content. Through extensive experimentation and evaluation, we demonstrate the effectiveness of the proposed approach in accurately identifying DeepFake videos with high precision and recall rates. Furthermore, our method exhibits robustness against various manipulation techniques, showcasing its potential for real-world applications in combating the spread of misinformation and fraudulent content. The implementation of InceptionResNetV2 for DeepFake detection presents a promising solution to the growing challenges posed by synthetic media, providing a reliable tool for safeguarding the integrity of visual information in digital environments.