Enhancing Global Security: A Robust CNN Model for Deepfake Video Detection

In recent years, the rapid advancements in machine learning (ML), artificial intelligence (AI), and deep learning (DL) have ushered in a new era of sophistication in image and video manipulation techniques. Notably, the emergence of Deepfake technology, driven by AI, has garnered substantial attention. Deepfakes involve training DL models on extensive datasets of similar faces and subsequently mapping one person's expressions onto another's face, resulting in deceptively realistic fake videos that can be virtually indistinguishable from authentic ones. The proliferation of Deepfakes poses various threats, including the potential to incite political turmoil, coerce individuals, or fabricate false terrorist incidents. This trend undermines privacy, consent, and the trustworthiness of digital media. Consequently, there is a pressing need to continually advance Deepfake detection and prevention methodologies to safeguard against their malevolent use and uphold the integrity of digital content. This paper introduces a Convolutional Neural Network (CNN) based DL model specifically developed for the classification of Deepfake video frames. Our model exhibits impressive performance, as validated by a thorough analysis on the VDFD dataset, where it achieves an outstanding average precision, recall, and F1-score of 95%, 94%, and 94%, respectively. Moreover, our model showcases its efficacy across various widely recognized Deepfake datasets, including FF++, Celeb-DF, and DFDC, with frame detection average rates for precision, recall, and F1-score ranging from 80% to 85%. These compelling results signify that our proposed CNN-based frame detection technique is a powerful tool for Deepfake detection, emphasizing the critical significance of automated Deepfake detection with an exceptionally high detection rate. This technology represents a pivotal step toward protecting against the potential misuse of Deepfakes, reinforcing the security and integrity of digital content in our modern world.