How spammers and scammers leverage AI-generated images on Facebook for audience growth

Author:

DiResta Renée1,Goldstein Josh A.2

Affiliation:

1. Stanford Internet Observatory, Stanford University, USA

2. Center for Security and Emerging Technology, Georgetown University, USA

Abstract

Much of the research and discourse on risks from artificial intelligence (AI) image generators, such as DALL-E and Midjourney, has centered around whether they could be used to inject false information into political discourse. We show that spammers and scammers—seemingly motivated by profit or clout, not ideology—are already using AI-generated images to gain significant traction on Facebook. At times, the Facebook Feed is recommending unlabeled AI-generated images to users who neither follow the Pages posting the images nor realize that the images are AI-generated, highlighting the need for improved transparency and provenance standards as AI models proliferate.

Publisher

Shorenstein Center for Media, Politics, and Public Policy

Reference29 articles.

1. Bickert, M. (2024, April 5). Our approach to labeling AI-generated content and manipulated media. Meta Newsroom. https://about.fb.com/news/2024/04/metas-approach-to-labeling-ai-generated-content-and-manipulated-media/

2. Caufield, M. (2019, June 19). SIFT (the four moves). Hapgood. https://hapgood.us/2019/06/19/sift-the-four-moves/

3. Clegg, N. (2024, February 6). Labeling AI-generated images on Facebook, Instagram and Threads. Meta Newsroom. https://about.fb.com/news/2024/02/labeling-ai-generated-images-on-facebook-instagram-and-threads/

4. Dixon, R. B. L., & Frase, H. (2024, March). An argument for hybrid AI incident reporting: Lessons learned from other incident reporting systems. Center for Security and Emerging Technology. https://cset.georgetown.edu/publication/an-argument-for-hybrid-ai-incident-reporting/

5. Ferrara, E. (2024). GenAI against humanity: Nefarious applications of generative artificial intelligence and large language models. Journal of Computational Science, 7, 549–569. https://doi.org/10.1007/s42001-024-00250-1

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3