<![CDATA[This study evaluates an Xception-based Multitask Self-Supervised Learning (MSSL) framework for detecting real and synthetic visual content across image and video modalities. Separate models were developed for CIFAKE images and FaceForensics++ videos. Beyond the primary binary-classification objective, MSSL integrates three auxiliary representation-learning tasks: rotation prediction, patch reconstruction, and artifact-map prediction. These tasks are intended to encourage the model to learn robust semantic and manipulation-related visual features. Experiments used class-balanced subsets of 5,000 CIFAKE images and 840 FaceForensics++ videos. For video classification, five frames were selected through uniform temporal sampling, and frame-level probabilities were averaged to obtain video-level predictions; a three-frame setting was also examined. A fixed decision threshold of 0.50 was defined a priori and applied consistently across both modalities. On CIFAKE, Xception+MSSL achieved 92.60% accuracy and 0.9797 ROC-AUC, outperforming the baseline Xception model, which obtained 79.60% accuracy and 0.8937 ROC-AUC. On FaceForensics++, five-frame MSSL achieved 68.25% video-level accuracy and 0.7365 ROC-AUC, compared with 62.70% and 0.6652 for Xception. The results indicate consistent within-dataset improvements, although cross-dataset and cross-generator generalization remain unverified.]]>
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