Please use this identifier to cite or link to this item: http://dspace.iitrpr.ac.in:8080/xmlui/handle/123456789/3841
Full metadata record
DC FieldValueLanguage
dc.contributor.authorWinkler, S.-
dc.contributor.authorChen, W.-
dc.contributor.authorDhall, D.-
dc.contributor.authorKorshunov, P.-
dc.date.accessioned2022-08-21T07:24:37Z-
dc.date.available2022-08-21T07:24:37Z-
dc.date.issued2022-08-21-
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/3841-
dc.description.abstracteepfakes, i.e.synthetic or "fake" media content generated using deep learning, are a double-edged sword. On one hand, they pose new threats and risks in the form of scams, fraud, disinformation, social manipulation, or celebrity porn. On the other hand, deepfakes have just as many meaningful and beneficial applications - they allow us to create and experience things that no longer exist, or that have never existed, enabling numerous exciting applications in entertainment, education, and even privacy. While most work has focused on fake images and video alone, the multi-modal, audiovisual aspect is very important to both convincing generation and accurate detection of fake multimedia content. Therefore, we organize ADGD21: 1st Workshop on Synthetic Multimedia - Audiovisual Deepfake Generation and Detection so as to provide a platform for researchers and engineers to share their ideas and approaches in this field.en_US
dc.language.isoen_USen_US
dc.titleADGD'21: 1st workshop on synthetic multimedia audiovisual deepfake generation and detection welcome messageen_US
dc.typeArticleen_US
Appears in Collections:Year-2021

Files in This Item:
File Description SizeFormat 
Full Text.pdf794.68 kBAdobe PDFView/Open    Request a copy


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.