Conspicuous progression in the field of machine learning and deep learning have led the jump of highly realistic fake media, these media oftentimes referred as deepfakes. Deepfakes are fabricated media which are generated by sophisticated AI that are at times very difficult to set apart from the real media. So far, this media can be uploaded to the various social media platforms, hence advertising it to the world got easy, calling for an efficacious countermeasure. Thus, one of the optimistic counter steps against deepfake would be deepfake detection. To undertake this threat, researchers in the past have created models to detect deepfakes based on ML/DL techniques like Convolutional Neural Networks. This paper aims to explore different methodologies with an intention to achieve a cost-effective model with a higher accuracy with different types of the datasets, which is to address the generalizability of the dataset.

翻译：机器学习与深度学习领域的显著进展催生了高度逼真的伪造媒体，这类媒体通常被称为深度伪造。深度伪造是由先进人工智能生成的合成媒体，有时极难与真实媒体区分。目前，这类媒体可上传至各类社交平台，使其全球传播变得轻而易举，因此亟需有效的应对措施。在此背景下，深度伪造检测成为对抗这一威胁的乐观策略之一。为应对这一挑战，研究者过去已基于机器学习/深度学习技术（如卷积神经网络）构建了深度伪造检测模型。本文旨在探索不同方法，以期在应对不同类型数据集时实现高精度的经济高效模型，从而解决数据集的泛化性问题。