Deep Fake Detection

Deepfakes, synthetic media generated through AI and machine learning, pose a significant challenge in digital content authenticity. Convolutional neural networks (CNNs). However, this paper explores a novel approach utilizing the InceptionV3 architecture for enhanced deepfake detection. InceptionV3 serves as a feature extractor, capturing intricate visual patterns, while a custom classifier distinguishes between authentic and manipulated content. Unlike previous methods relying solely on CNNs, this hybrid approach integrates InceptionV3 with its robust feature extraction capabilities alongside traditional CNNs. The effectiveness of this methodology is evaluated using benchmark datasets such as Celeb-DF and FaceForensics++, showcasing its potential in combating the proliferation of deepfake media.