Towards a New Science of Disinformation

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Authors: Claudio S. Pinhanez, German H. Flores, Marisa A. Vasconcelos, Mu Qiao, Nick Linck, Rogério de Paula, Yuya J. Ong

Published: 2022-03-17 19:10:29+00:00

AI Summary

This research paper proposes a new "Science of Disinformation" to address the growing threat of deepfakes. It argues for a shift from combating the spread of fake media to a prevention and cure framework, focusing on providing users with tools to verify and challenge the veracity of information.

Abstract

How can we best address the dangerous impact that deep learning-generated fake audios, photographs, and videos (a.k.a. deepfakes) may have in personal and societal life? We foresee that the availability of cheap deepfake technology will create a second wave of disinformation where people will receive specific, personalized disinformation through different channels, making the current approaches to fight disinformation obsolete. We argue that fake media has to be seen as an upcoming cybersecurity problem, and we have to shift from combating its spread to a prevention and cure framework where users have available ways to verify, challenge, and argue against the veracity of each piece of media they are exposed to. To create the technologies behind this framework, we propose that a new Science of Disinformation is needed, one which creates a theoretical framework both for the processes of communication and consumption of false content. Key scientific and technological challenges facing this research agenda are listed and discussed in the light of state-of-art technologies for fake media generation and detection, argument finding and construction, and how to effectively engage users in the prevention and cure processes.

Key findings

UNKNOWN. The paper is a proposal for future research and does not present empirical findings. It highlights the limitations of current methods in combating deepfakes and emphasizes the need for a new theoretical framework and technological advancements.

Approach

The authors propose a two-pronged approach: developing a theoretical framework encompassing disinformation communication and consumption, and creating technological solutions (prophylactics and antigens) to automatically detect, challenge, and help users overcome belief in fake media.

Datasets

UNKNOWN

Model(s)

Generative Adversarial Networks (GANs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short Term Memory (LSTM), Gated Recurrent Units (GRU), Support Vector Machine (SVM), VGGFace, VGG-19

Author countries

Brazil, USA

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