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:机器学习和深度学习领域的显著进展导致高度逼真的假媒体的出现,这些媒体通常被称为深度假像。深度假像是由复杂的人工智能生成的虚假媒体,有时很难与真实媒体区分开来。迄今为止,这些媒体可以上传到各种社交媒体平台,因此向世界广告推销变得非常容易,需要一种有效的对策。因此,对深度假像的一种乐观反击步骤将是深度假像检测。为了应对这种威胁,过去的研究人员创建了基于卷积神经网络等ML / DL技术的模型来检测深度假像。本文旨在探讨不同的方法来实现成本效益高、准确性更高的模型,并使用不同类型的数据集来解决数据集的通用性问题。