Deepfakes: Trick or treat?

By Jan H. Kietzmann, Linda W. Lee, Ian Paul McCarthy, Tim C Kietzmann. Business Horizons, 2020

387 citations recorded by the source corpus.

Although manipulations of visual and auditory media are as old as the media themselves, the recent entrance of deepfakes has marked a turning point in the creation of fake content. Powered by latest technological advances in AI and machine learning, they offer automated procedures to create fake content that is harder and harder to detect to human observers. The possibilities to deceive are endless, including manipulated pictures, videos and audio, that will have large societal impact. Because of this, organizations need to understand the inner workings of the underlying techniques, as well as their strengths and limitations. This article provides a working definition of deepfakes together with an overview of the underlying technology. We classify different deepfake types: photo (face- and body-swapping), audio (voice-swapping, text to speech), video (face-swapping, face-morphing, full body puppetry) and audio and video (lip-synching), and identify risks and opportunities to help organizations think about the future of deepfakes. Finally, we propose the R.E.A.L. framework to manage deepfake risks: Record original content to assure deniability, Expose deepfakes early, Advocate for legal protection and Leverage trust to counter credulity. Following these principles, we hope that our society can be more prepared to counter the deepfake tricks as we appreciate its treats.

Subfields: Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/ad234ca8ab3829907bdab0d235cf5457f7503614

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Citations in the corpus

DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection (2020, methodology): “The present survey complements [39] and [40] with a more detailed review of each facial manipulation group, including”
Digital technologies: tensions in privacy and data (2022, background): “…such as people ’ s political opinions, sexual orientation, and medical conditions from less sensitive information (Davenport et al., 2020), then manipulate users through predictive analytics or create deception such as deep fakes (Kietzmann et al., 2020), which threaten information privacy .”
Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes (2022, methodology): “fake project reason about the ethics of their work? To answer this, we conduct an interview study in an open source community which builds software to create “Deepfakes”: videos which replace the likeness of one person with another [43].”
Artificial intelligence in the creative industries: a review (2020, background): “Creation Content generation [16–18] [19–32] [25,28], TM [33], RL [34–37], for text, audio, video [38–42] BERT [43], VAE [44] and game [30] NEAT [45] Animation [46–50] [51,52] [53] VAE [52] AR/VR [54,55] Deepfakes [56] [57] VAE [58] Content and captions [59] [60–62] [63,64] VAE [59]”
Deepfake detection using deep learning methods: A systematic and comprehensive review (2023, background): “Face manipulation has received much attention because of its high semantic value and wide range of uses (Kietzmann et al., 2020).”