Embracing deepfakes and AI-generated images in neuroscience research

被引:4
|
作者
Becker, Casey [1 ]
Laycock, Robin [1 ]
机构
[1] RMIT Univ, Melbourne, Australia
关键词
artificial neural networks; dynamic stimuli; perception; research methods; vision;
D O I
10.1111/ejn.16052
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
The rise of deepfakes and AI-generated images has raised concerns regarding their potential misuse. However, this commentary highlights the valuable opportunities these technologies offer for neuroscience research. Deepfakes deliver accessible, realistic and customisable dynamic face stimuli, while generative adversarial networks (GANs) can generate and modify diverse and high-quality static content. These advancements can enhance the variability and ecological validity of research methods and enable the creation of previously unattainable stimuli. When AI-generated images are informed by brain responses, they provide unique insights into the structure and function of visual systems. The authors argue that experimental psychologists and cognitive neuroscientists stay informed about these emerging tools and embrace their potential to advance the field of visual neuroscience.
引用
收藏
页码:2657 / 2661
页数:5
相关论文
共 50 条
  • [31] TWIGMA: A dataset of AI-Generated Images with Metadata From Twitter
    Chen, Yiqun T.
    Zou, James
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 36 (NEURIPS 2023), 2023,
  • [32] Advanced Detection of AI-Generated Images Through Vision Transformers
    Lamichhane, Darshan
    IEEE ACCESS, 2025, 13 : 3644 - 3652
  • [33] TWIGMA: A dataset of AI-Generated Images with Metadata From Twitter
    Department of Biomedical Data Science, Stanford University, Stanford
    CA
    94305, United States
    不详
    CA
    94305, United States
    arXiv,
  • [34] Steganographic secret sharing via AI-generated photorealistic images
    Kai Gao
    Ching-Chun Chang
    Ji-Hwei Horng
    Isao Echizen
    EURASIP Journal on Wireless Communications and Networking, 2022
  • [35] A Human-factors Approach for Evaluating AI-generated Images
    Combs, Kara
    Bihl, Trevor J.
    Gadre, Arya
    Christopherson, Isaiah
    PROCEEDINGS OF THE 2024 COMPUTERS AND PEOPLE RESEARCH CONFERENCE, SIGMIS-CPR 2024, 2024,
  • [36] Research can help to tackle AI-generated disinformation
    Feuerriegel, Stefan
    DiRresta, Renee
    Goldstein, Josh A.
    Kumar, Srijan
    Lorenz-Spreen, Philipp
    Tomz, Michael
    Proellochs, Nicolas
    NATURE HUMAN BEHAVIOUR, 2023, 7 (11) : 1818 - 1821
  • [37] Research can help to tackle AI-generated disinformation
    Stefan Feuerriegel
    Renée DiResta
    Josh A. Goldstein
    Srijan Kumar
    Philipp Lorenz-Spreen
    Michael Tomz
    Nicolas Pröllochs
    Nature Human Behaviour, 2023, 7 : 1818 - 1821
  • [38] Understanding the Impact of AI-Generated Deepfakes on Public Opinion, Political Discourse, and Personal Security in Social Media
    Kharvi, Prakash L.
    IEEE SECURITY & PRIVACY, 2024, 22 (04) : 115 - 122
  • [39] AI-Generated Clinical Summaries
    Chen, Charlaine
    Thornton, Joseph E.
    JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2024, 331 (22): : 1967 - 1968
  • [40] Not a generative AI-generated Editorial
    不详
    NATURE CANCER, 2023, 4 (02) : 151 - 152