The Age of Synthetic Realities: Challenges and Opportunities

João Phillipe Cardenuto*, Jing Yang, Rafael Padilha, Renjie Wan, Daniel Moreira, Haoliang Li, Shiqi Wang, Fernanda Andaló, Sébastien Marcel, Anderson Rocha

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

10 Citations (Scopus)
33 Downloads (CityUHK Scholars)

Abstract

Synthetic realities are digital creations or augmentations that are contextually generated through the use of Artificial Intelligence (AI) methods, leveraging extensive amounts of data to construct new narratives or realities, regardless of the intent to deceive. In this paper, we delve into the concept of synthetic realities and their implications for Digital Forensics and society at large within the rapidly advancing field of AI. We highlight the crucial need for the development of forensic techniques capable of identifying harmful synthetic creations and distinguishing them from reality. This is especially important in scenarios involving the creation and dissemination of fake news, disinformation, and misinformation. Our focus extends to various forms of media, such as images, videos, audio, and text, as we examine how synthetic realities are crafted and explore approaches to detecting these malicious creations. Additionally, we shed light on the key research challenges that lie ahead in this area. This study is of paramount importance due to the rapid progress of AI generative techniques and their impact on the fundamental principles of Forensic Science. © 2023 J. P. Cardenuto et al.
Original languageEnglish
Article numbere44
JournalAPSIPA Transactions on Signal and Information Processing
Volume12
Issue number1
DOIs
Publication statusPublished - 6 Nov 2023

Funding

This work was supported by the São Paulo Research Foundation (FAPESP)(J.P.C., grant numbers 2020/02211-2, 2017/12646-3), (J.Y., grant numbers 2019/04053-8, 2022/05002-0), (A.R., grant number 2017/12646-3); Blue Sky Research Fund of HKBU (R.W., grant number BSRF/21-22/16); Guangdong Basic and Applied Basic Research Foundation (R.W., grant number2022A1515110692); Defense Advanced Research Projects Agency (DARPA)and the Air Force Research Laboratory (AFRL) (D.M., grant number FA8750-20-2-1004); and the Research Grant Council (RGC) of Hong Kong through Early Career Scheme (ECS) (H.L., grant number 21200522). Also, part of this work within the Idiap Research Institute received funding from the Swiss State Secretariat for Education, Research and Innovation (SERI), the Swiss State of Valais and the City of Martigny.

Publisher's Copyright Statement

  • This full text is made available under CC-BY-NC 4.0. https://creativecommons.org/licenses/by-nc/4.0/

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