AIGCs Confuse AI Too: Investigating and Explaining Synthetic
  Image-induced Hallucinations in Large Vision-Language Models
Yifei Gao Jiaqi Wang Zhiyu Lin Jitao Sang
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 underscore the exacerbated
hallucination phenomena in Large Vision-Language Models (LVLMs) caused by
AI-synthetic images. Remarkably, our findings shed light on a consistent AIGC
\textbf{hallucination bias}: the object hallucinations induced by synthetic
images are characterized by a greater quantity and a more uniform position
distribution, even these synthetic images do not manifest unrealistic or
additional relevant visual features compared to natural images. Moreover, our
investigations on Q-former and Linear projector reveal that synthetic images
may present token deviations after visual projection, thereby amplifying the
hallucination bias.