本論文旨在研究不同的機器學習技術和策略對於深偽造檢測算法的泛化能力的影響。本研究將評估轉移學習、數據增強和集成等技術在提高深偽造檢測算法的泛化能力方面的效果。本研究的結果對於深偽造檢測算法的發展和對抗深偽造的努力具有重要意義。通過提高深偽造檢測算法的泛化能力，我們可以增加它們對於檢測未知深偽造的效果，從而減輕深偽造對社會的有害影響。

This thesis aims to study the impact of various machine learning techniques and strategies on the generalization capabilities of deepfake detection algorithms. The study will evaluate the effectiveness of techniques such as transfer learning, data augmentation,and ensembling in improving the generalization of deepfake detection algorithms.The results of this study will have important implications for the development of deepfake detection algorithms and the fight against deepfakes. By improving the generalization capabilities of deepfake detection algorithms, we can increase their effectiveness in detecting unseen deepfakes, thereby mitigating their harmful effects on society.

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