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This paper presents a study on detecting deepfake videos using advanced CNN architectures, specifically EfficientNet-B4 and XceptionNet, evaluated on the FF++ and Celeb-DF (v2) datasets. The methodology involves preprocessing, frame extraction, and performance evaluation using metrics such as AUC and log loss, demonstrating the effectiveness of both models in accurately classifying real and fake videos. The study emphasizes the need for continuous updates to deepfake detection algorithms to keep pace with e
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