AIGCs Confuse AI Too: Investigating and Explaining Synthetic Image-induced Hallucinations in Large Vision-Language Models

arXiv:2403.08542v1 Announce Type: new
Abstract: The evolution of Artificial Intelligence Generated Contents (AIGCs) is advancing towards higher quality. The growing interactions with AIGCs present a new challenge to the data-driven AI community: While AI-generated contents have played a crucial role in a wide range of AI models, the potential hidden risks they introduce have not been thoroughly examined. Beyond human-oriented forgery detection, AI-generated content poses potential issues for AI models originally designed to process natural data. In this study, we …

abstract ai community artificial artificial intelligence arxiv challenge community contents cs.cv data data-driven evolution generated hallucinations image intelligence interactions language language models quality role synthetic type vision vision-language models