Deepfake Video Detection Using Ada-Boosting on the DFDC Dataset

The rapid proliferation of deepfake videos, generated using advanced machine learning techniques to create highly realistic but misleading content, poses significant challenges across various sectors, including cybersecurity, media integrity, and personal privacy. Detecting these deepfakes has become essential for maintaining trust in digital media and preventing malicious exploitation. This paper presents a novel approach to deepfake video detection by employing the AdaBoost algorithm, a powerful ensemble learning method recognized for its ability to improve classification performance by focusing on difficult-to-classify instances. Using the Deepfake Detection Challenge (DFDC) dataset, our study demonstrates that the AdaBoost classifier, when coupled with a carefully designed feature set, achieves competitive accuracy in detecting deepfake videos. Our results show that this approach provides an effective solution for deepfake detection, with strong recall performance, making it a viable method for real-world applications.