Multi-modal Learning for DeepFake Detection in Intelligent Information Processing Lab Seminar

This Master's Thesis Review discusses the use of multi-modal learning to efficiently detect DeepFake content, addressing challenges such as DeepFake generation techniques and the limitations of traditional methods. The research explores how Vision, Audio, and Text modalities can be integrated for more accurate detection, emphasizing the importance of diverse training data and avoiding individual biases. Various techniques such as Score Level Fusion and Feature Level Fusion are examined, along with the extraction and embedding of different modalities for effective DeepFake detection.

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