@inproceedings{
gao2024aigcs,
title={{AIGC}s Confuse {AI} Too: Investigating and Explaining Synthetic Image-induced Hallucinations in Large Vision-Language Models},
author={Yifei Gao and Jiaqi Wang and Zhiyu Lin and Jitao Sang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=5qOWYC2IQj}
}
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 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.
AIGCs Confuse AI Too: Investigating and Explaining Synthetic Image-induced Hallucinations in Large Vision-Language Models
Yifei Gao Jiaqi Wang Zhiyu Lin Jitao Sang