April 8, 2024, 4:42 a.m. | Zhengyuan Jiang, Moyang Guo, Yuepeng Hu, Neil Zhenqiang Gong

cs.LG updates on arXiv.org arxiv.org

arXiv:2404.04254v1 Announce Type: cross
Abstract: Several companies--such as Google, Microsoft, and OpenAI--have deployed techniques to watermark AI-generated content to enable proactive detection. However, existing literature mainly focuses on user-agnostic detection. Attribution aims to further trace back the user of a generative-AI service who generated a given content detected as AI-generated. Despite its growing importance, attribution is largely unexplored. In this work, we aim to bridge this gap by providing the first systematic study on watermark-based, user-aware detection and attribution of …

abstract ai-generated content arxiv attribution companies cs.ai cs.cl cs.cr cs.cv cs.lg detection generated generative generative-ai google however literature microsoft openai service type watermark

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Data Scientist

@ Publicis Groupe | New York City, United States

Bigdata Cloud Developer - Spark - Assistant Manager

@ State Street | Hyderabad, India