Deepfake Detection Using XceptionNet

The rapid rise of synthetic media, especially deepfakes, has sparked major concerns around misinformation, identity fraud, and diminishing public confidence in visual content. As these altered videos grow increasingly realistic, there is a pressing demand for reliable and scalable detection methods. This paper explores the use of the XceptionNet convolutional neural network architecture for deepfake detection. The analysis is based on the FaceForensics++ dataset, which comprises more than 1.8 million manipulated images created with four sophisticated face manipulation methods: NeuralTextures, FaceSwap, Face2Face, and DeepFakes. Cropped facial images are used for binary classification which is a process of differentiating between authentic and fraudulent content. Experimental results; with an accuracy of over 95% on unprocessed, and high-quality videos; over 80% accuracy even when heavily compressed; demonstrate that XceptionNet significantly outperforms both human observers and traditional detection methods, particularly under conditions of image compression. These findings highlight the robustness of deep learning-based models and the critical role of domain-specific preprocessing in improving detection accuracy.