Fake Face Detection Based on Videos Using OpenCV and Neural Network Architecture

The rapid development of the Internet has enabled the widespread distribution of manipulated facial images, particularly Deepfakes, which are increasingly difficult to detect using conventional methods. While current approaches focus on spatial domain features or complex network architectures, they often lack robustness against sophisticated forgery techniques. To address this, we propose a MobileNetV2-based Deepfake detection framework that leverages efficient convolutional feature extraction for accurate classification of real and fake facial images. The framework begins with OpenCV-based preprocessing, including face detection, alignment, and normalization, to ensure consistent input quality and enhance the discriminative features for detection. MobileNetV2, a lightweight yet powerful convolutional neural network, is employed to automatically learn hierarchical spatial features from the preprocessed facial images, eliminating the need for handcrafted features. By combining OpenCV preprocessing with MobileNetV2, the proposed system effectively captures subtle visual artifacts and texture inconsistencies introduced by Deepfake manipulation. This approach enables robust and scalable detection, generalizing well across diverse datasets and real-world scenarios, providing a practical solution for automated Deepfake detection in security, media verification, and social media monitoring applications.