• The Positive Disruptive Potential of Deepfakes and Synthetic Data 

    Author(s):
    Aaron Tucker (see profile)
    Date:
    2021
    Group(s):
    CSDH-SCHN 2021: Making the Network
    Subject(s):
    Machine learning, Open access publishing
    Item Type:
    Conference proceeding
    Conf. Title:
    CSDH/SCHN
    Conf. Org.:
    CSDH/SCHN
    Conf. Loc.:
    Zoom
    Conf. Date:
    May 30th - June 3 2021
    Tag(s):
    Deepfakes, facial recognition, Open Acces, synthetic data, Open access
    Permanent URL:
    http://dx.doi.org/10.17613/0673-nk40
    Abstract:
    As Mika Wusterland demonstrates, popular discourses around deepfakes are primarily concerned with the ability to create a historical event that can pass as “real” (39). Yet, as Vivian Sobchak argues, “the ‘events’ of the twentieth century are less inherently novel than the novel technologies of representations that have transformed ‘events’” (4); in the twenty-first century, deepfakes are a representational technology that ruptures traditional understandings of historical events. From this perspective, deepfakes capture “the loss of a determinate historical document” and, in turn, surface the contemporary instability inherent to representing historical events (ibid., 6). This paper will explore how foregrounding deepfakes’ spectacular digitally-generated verisimilitude allows for the technology to become a potential cinematic technique able to intervene as a tactic for providing anonymous witness testimony; as a database visualization technique; as a form of documentary reenactment; and as counterfactual and alternate historical texts. T
    Metadata:
    xml
    Status:
    Published
    Last Updated:
    2 years ago
    License:
    All Rights Reserved
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