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.

人工智能生成内容的进化朝着更高质量的方向发展，与人工智能生成内容的不断交互给数据驱动的人工智能社区带来了新的挑战：尽管 AI 生成的内容在广泛的 AI 模型中扮演着关键角色，但它们引入的潜在风险尚未得到充分的审查。本研究突出了由 AI 合成图像引起的大型视觉语言模型中的严重幻觉现象，结果发现合成图像引起的物体幻觉特点是数量更多且位置分布更均匀，即使这些合成图像与自然图像相比没有呈现非现实或额外相关的视觉特征。此外，我们对 Q-former 和线性投影仪的研究发现，视觉投影后合成图像可能呈现令牌偏差，从而放大幻觉偏差。