Deepfake Video Detection Using InceptionResnetV2: A Convolutional Neural Network Approach

• Published in: Proceedings (International Confernce on Computational Intelligence and Communication Networks) pp. 595 - 601
• Main Authors: Praveen kumar, Mandala, Kumari, V Valli, Lakshmi Durga, K Swathi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 22.12.2024
• Subjects: convolutional neural network; Convolutional neural networks; Deepfake; Deepfakes; Face recognition; image classification; Inception_ResNet_v2; Media; Robots; Solid modeling; Streaming media; Technological innovation; Transfer learning; video detection; Virtual reality
• ISSN: 2472-7555
• DOI: 10.1109/CICN63059.2024.10847574

Deepfake videos, generated by sophisticated AI algorithms, pose significant challenges to the authenticity and trustworthiness of multimedia content on the internet. In this study, we propose a deepfake video detection system utilizing the Inception_ResNet_v2 architecture, a deep convolutional neural network renowned for its effectiveness in image classification tasks. The system is trained and evaluated on the Deepfake Challenge Dataset sourced from Kaggle, comprising a diverse set of real and manipulated video clips. We preprocess the dataset, extracting frames from each video and augmenting them to enhance model generalization. The Inception_ResNet_v2 model is then fine-tuned using transfer learning, leveraging pre-trained weights from ImageNet to expedite convergence and improve performance. Through extensive experimentation and evaluation, we demonstrate the efficacy of our approach in accurately distinguishing between genuine and deep-fake videos. The proposed system achieves promising results in detecting deep-fake videos, showcasing its potential utility in combating the proliferation of synthetic media and safeguarding the integrity of digital content. Our findings underscore the importance of leveraging advanced machine learning techniques for addressing the evolving challenges posed by deep-fake technology.