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
  • [21] Seeing is No Longer Believing: A Survey on the State of Deepfakes, AI-Generated Humans, and Other Nonveridical Media
    Pocol, Andreea
    Istead, Lesley
    Siu, Sherman
    Mokhtari, Sabrina
    Kodeiri, Sara
    ADVANCES IN COMPUTER GRAPHICS, CGI 2023, PT II, 2024, 14496 : 427 - 440
  • [22] Reliability of AI-generated magnetograms from only EUV images
    Liu, Jiajia
    Wang, Yimin
    Huang, Xin
    Korsos, Marianna B.
    Jiang, Ye
    Wang, Yuming
    Erdelyi, Robert
    NATURE ASTRONOMY, 2021, 5 (02) : 108 - 110
  • [23] Simulation training in mammography with AI-generated images: a multireader study
    Rangarajan, Krithika
    Manivannan, Veeramakali Vignesh
    Singh, Harpinder
    Gupta, Amit
    Maheshwari, Hrithik
    Gogoi, Rishparn
    Gogoi, Debashish
    Das, Rupam Jyoti
    Hari, Smriti
    Vyas, Surabhi
    Sharma, Raju
    Pandey, Shivam
    Seenu, V.
    Banerjee, Subhashis
    Namboodiri, Vinay
    Arora, Chetan
    EUROPEAN RADIOLOGY, 2025, 35 (02) : 562 - 571
  • [24] Study of Subjective and Objective Naturalness Assessment of AI-Generated Images
    Chen, Zijian
    Sun, Wei
    Wu, Haoning
    Zhang, Zicheng
    Jia, Jun
    Huang, Ru
    Min, Xiongkuo
    Zhai, Guangtao
    Zhang, Wenjun
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2025, 35 (04) : 3573 - 3588
  • [25] Assessing the visual appeal of real/AI-generated food images
    Califano, Giovanbattista
    Spence, Charles
    FOOD QUALITY AND PREFERENCE, 2024, 116
  • [26] Reliability of AI-generated magnetograms from only EUV images
    Jiajia Liu
    Yimin Wang
    Xin Huang
    Marianna B. Korsós
    Ye Jiang
    Yuming Wang
    Robert Erdélyi
    Nature Astronomy, 2021, 5 : 108 - 110
  • [27] Prompting Bias: Assessing representation and accuracy in AI-generated images
    York, Eric J.
    Brumberger, Eva
    Harris, La Verne Abe
    PROCEEDINGS OF THE 42ND INTERNATIONAL CONFERENCE ON DESIGN OF COMMUNICATION, SIGDOC 2024, 2024, : 106 - 115
  • [28] TWIGMA: A dataset of AI-Generated Images with Metadata From Twitter
    Chen, Yiqun T.
    Zou, James
    Advances in Neural Information Processing Systems, 2023, 36
  • [29] A Dataset and Quality Evaluation of AI-generated Images for Online Learning
    Vu Hai Dang
    Onuoha, Chibuike
    Nguyen Duc Nam
    Truong Thu Huong
    Truong Cong Thang
    Pham Ngoc Nam
    2024 IEEE TENTH INTERNATIONAL CONFERENCE ON COMMUNICATIONS AND ELECTRONICS, ICCE 2024, 2024, : 487 - 491
  • [30] Steganographic secret sharing via AI-generated photorealistic images
    Gao, Kai
    Chang, Ching-Chun
    Horng, Ji-Hwei
    Echizen, Isao
    EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING, 2022, 2022 (01)