Perceptions and Realities of Text-to-Image Generation

被引:6
|
作者
Oppenlaender, Jonas [1 ]
Silvennoinen, Johanna [1 ]
Paananen, Ville [2 ]
Visuri, Aku [2 ]
机构
[1] Univ Jyvaskyla, Jyvaskyla, Finland
[2] Univ Oulu, Oulu, Finland
关键词
generative AI; text-to-image generation;
D O I
10.1145/3616961.3616978
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Generative artificial intelligence (AI) is a widely popular technology that will have a profound impact on society and individuals. Less than a decade ago, it was thought that creative work would be among the last to be automated - yet today, we see AI encroaching on many creative domains. In this paper, we present the findings of a survey study on people's perceptions of text-to-image generation. We touch on participants' technical understanding of the emerging technology, their fears and concerns, and thoughts about risks and dangers of text-to-image generation to the individual and society. We find that while participants were aware of the risks and dangers associated with the technology, only few participants considered the technology to be a personal risk. The risks for others were more easy to recognize for participants. Artists were particularly seen at risk. Interestingly, participants who had tried the technology rated its future importance lower than those who had not tried it. This result shows that many people are still oblivious of the potential personal risks of generative artificial intelligence and the impending societal changes associated with this technology.
引用
收藏
页码:279 / 288
页数:10
相关论文
共 50 条
  • [1] Controllable Text-to-Image Generation
    Li, Bowen
    Qi, Xiaojuan
    Lukasiewicz, Thomas
    Torr, Philip H. S.
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), 2019, 32
  • [2] Surgical text-to-image generation
    Nwoye, Chinedu Innocent
    Bose, Rupak
    Elgohary, Kareem
    Arboit, Lorenzo
    Carlino, Giorgio
    Lavanchy, Joel L.
    Mascagni, Pietro
    Padoy, Nicolas
    PATTERN RECOGNITION LETTERS, 2025, 190 : 73 - 80
  • [3] Expressive Text-to-Image Generation with Rich Text
    Ge, Songwei
    Park, Taesung
    Zhu, Jun-Yan
    Huang, Jia-Bin
    2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION, ICCV, 2023, : 7511 - 7522
  • [4] SEMANTICALLY INVARIANT TEXT-TO-IMAGE GENERATION
    Sah, Shagan
    Peri, Dheeraj
    Shringi, Ameya
    Zhang, Chi
    Dominguez, Miguel
    Savakis, Andreas
    Ptucha, Ray
    2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2018, : 3783 - 3787
  • [5] Shifted Diffusion for Text-to-image Generation
    Zhou, Yufan
    Liu, Bingchen
    Zhu, Yizhe
    Yang, Xiao
    Chen, Changyou
    Xu, Jinhui
    2023 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2023, : 10157 - 10166
  • [6] Text-to-Image Generation for Abstract Concepts
    Liao, Jiayi
    Chen, Xu
    Fu, Qiang
    Du, Lun
    He, Xiangnan
    Wang, Xiang
    Han, Shi
    Zhang, Dongmei
    THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 4, 2024, : 3360 - 3368
  • [7] Semantics Disentangling for Text-to-Image Generation
    Yin, Guojun
    Liu, Bin
    Sheng, Lu
    Yu, Nenghai
    Wang, Xiaogang
    Shao, Jing
    2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019), 2019, : 2322 - 2331
  • [8] Optimizing Prompts for Text-to-Image Generation
    Hao, Yaru
    Chi, Zewen
    Dong, Li
    Wei, Furu
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 36 (NEURIPS 2023), 2023,
  • [9] Prompt Refinement with Image Pivot for Text-to-Image Generation
    Zhan, Jingtao
    Ai, Qingyao
    Liu, Yiqun
    Pan, Yingwei
    Yao, Ting
    Mao, Jiaxin
    Ma, Shaoping
    Mei, Tao
    PROCEEDINGS OF THE 62ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1: LONG PAPERS, 2024, : 941 - 954
  • [10] Development and Classification of Image Dataset for Text-to-Image Generation
    Kumar M.
    Mittal M.
    Singh S.
    Journal of The Institution of Engineers (India): Series B, 2024, 105 (04) : 787 - 796