In recent years, the use of online multimedia content has grown exponentially. This rapid growth has played a key role in amplifying the society impact of fake news. Among the emerging techniques used to manipulate digital information, the generation of synthetic images and videos, commonly referred to as Deepfakes, has become particularly prominent. Recently, audio content has also been affected by the possibility of fake content. In this study, various Deepfake generation techniques are analyzed to detect multimedia content. This way, particular attention is paid to audio content. Specifically, a dataset comprising authentic and synthetic fake multimedia content (images, videos and audio) is proposed. This dataset is available for public use in our Synthetic and Authentic Forensic Lab (SAFL) repository. It is planned to migrate the dataset to our institutional repository in the near future. Additionally, a reference model based on the three types of content hosted in this dataset is proposed to assess the effectiveness of Artificial Intelligence (AI) techniques in determining whether a given resource is fake or authentic.