评估生成人工智能和彩色图案在社交媒体上传播战争图像和虚假信息中的作用。

IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Frontiers in Artificial Intelligence Pub Date : 2025-01-06 eCollection Date: 2024-01-01 DOI:10.3389/frai.2024.1457247
Estibaliz García-Huete, Sara Ignacio-Cerrato, David Pacios, José Luis Vázquez-Poletti, María José Pérez-Serrano, Andrea Donofrio, Clemente Cesarano, Nikolaos Schetakis, Alessio Di Iorio
{"title":"评估生成人工智能和彩色图案在社交媒体上传播战争图像和虚假信息中的作用。","authors":"Estibaliz García-Huete, Sara Ignacio-Cerrato, David Pacios, José Luis Vázquez-Poletti, María José Pérez-Serrano, Andrea Donofrio, Clemente Cesarano, Nikolaos Schetakis, Alessio Di Iorio","doi":"10.3389/frai.2024.1457247","DOIUrl":null,"url":null,"abstract":"<p><p>This study explores the evolving role of social media in the spread of misinformation during the Ukraine-Russia conflict, with a focus on how artificial intelligence (AI) contributes to the creation of deceptive war imagery. Specifically, the research examines the relationship between color patterns (LUTs) in war-related visuals and their perceived authenticity, highlighting the economic, political, and social ramifications of such manipulative practices. AI technologies have significantly advanced the production of highly convincing, yet artificial, war imagery, blurring the line between fact and fiction. An experimental project is proposed to train a generative AI model capable of creating war imagery that mimics real-life footage. By analyzing the success of this experiment, the study aims to establish a link between specific color patterns and the likelihood of images being perceived as authentic. This could shed light on the mechanics of visual misinformation and manipulation. Additionally, the research investigates the potential of a serverless AI framework to advance both the generation and detection of fake news, marking a pivotal step in the fight against digital misinformation. Ultimately, the study seeks to contribute to ongoing debates on the ethical implications of AI in information manipulation and to propose strategies to combat these challenges in the digital era.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"7 ","pages":"1457247"},"PeriodicalIF":3.0000,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11743509/pdf/","citationCount":"0","resultStr":"{\"title\":\"Evaluating the role of generative AI and color patterns in the dissemination of war imagery and disinformation on social media.\",\"authors\":\"Estibaliz García-Huete, Sara Ignacio-Cerrato, David Pacios, José Luis Vázquez-Poletti, María José Pérez-Serrano, Andrea Donofrio, Clemente Cesarano, Nikolaos Schetakis, Alessio Di Iorio\",\"doi\":\"10.3389/frai.2024.1457247\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This study explores the evolving role of social media in the spread of misinformation during the Ukraine-Russia conflict, with a focus on how artificial intelligence (AI) contributes to the creation of deceptive war imagery. Specifically, the research examines the relationship between color patterns (LUTs) in war-related visuals and their perceived authenticity, highlighting the economic, political, and social ramifications of such manipulative practices. AI technologies have significantly advanced the production of highly convincing, yet artificial, war imagery, blurring the line between fact and fiction. An experimental project is proposed to train a generative AI model capable of creating war imagery that mimics real-life footage. By analyzing the success of this experiment, the study aims to establish a link between specific color patterns and the likelihood of images being perceived as authentic. This could shed light on the mechanics of visual misinformation and manipulation. Additionally, the research investigates the potential of a serverless AI framework to advance both the generation and detection of fake news, marking a pivotal step in the fight against digital misinformation. Ultimately, the study seeks to contribute to ongoing debates on the ethical implications of AI in information manipulation and to propose strategies to combat these challenges in the digital era.</p>\",\"PeriodicalId\":33315,\"journal\":{\"name\":\"Frontiers in Artificial Intelligence\",\"volume\":\"7 \",\"pages\":\"1457247\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2025-01-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11743509/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3389/frai.2024.1457247\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/frai.2024.1457247","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

本研究探讨了在乌克兰-俄罗斯冲突期间,社交媒体在错误信息传播中的不断演变的作用,重点是人工智能(AI)如何有助于创造欺骗性的战争图像。具体而言,该研究考察了战争相关视觉图像中的颜色模式(lut)与其感知真实性之间的关系,强调了这种操纵行为的经济、政治和社会后果。人工智能技术极大地推动了高可信度但人为的战争图像的制作,模糊了事实与虚构之间的界限。提出了一个实验项目,以训练能够创建模拟真实镜头的战争图像的生成人工智能模型。通过分析这个实验的成功,该研究旨在建立特定颜色模式和图像被认为是真实的可能性之间的联系。这可能会揭示视觉错误信息和操纵的机制。此外,该研究还调查了无服务器人工智能框架在推进假新闻生成和检测方面的潜力,标志着打击数字错误信息的关键一步。最终,该研究旨在为正在进行的关于人工智能在信息操纵中的伦理影响的辩论做出贡献,并提出应对数字时代这些挑战的策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Evaluating the role of generative AI and color patterns in the dissemination of war imagery and disinformation on social media.

This study explores the evolving role of social media in the spread of misinformation during the Ukraine-Russia conflict, with a focus on how artificial intelligence (AI) contributes to the creation of deceptive war imagery. Specifically, the research examines the relationship between color patterns (LUTs) in war-related visuals and their perceived authenticity, highlighting the economic, political, and social ramifications of such manipulative practices. AI technologies have significantly advanced the production of highly convincing, yet artificial, war imagery, blurring the line between fact and fiction. An experimental project is proposed to train a generative AI model capable of creating war imagery that mimics real-life footage. By analyzing the success of this experiment, the study aims to establish a link between specific color patterns and the likelihood of images being perceived as authentic. This could shed light on the mechanics of visual misinformation and manipulation. Additionally, the research investigates the potential of a serverless AI framework to advance both the generation and detection of fake news, marking a pivotal step in the fight against digital misinformation. Ultimately, the study seeks to contribute to ongoing debates on the ethical implications of AI in information manipulation and to propose strategies to combat these challenges in the digital era.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
6.10
自引率
2.50%
发文量
272
审稿时长
13 weeks
期刊最新文献
Examining the integration of artificial intelligence in supply chain management from Industry 4.0 to 6.0: a systematic literature review. The technology acceptance model and adopter type analysis in the context of artificial intelligence. An analysis of artificial intelligence automation in digital music streaming platforms for improving consumer subscription responses: a review. Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm. SineKAN: Kolmogorov-Arnold Networks using sinusoidal activation functions.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1