Large vision-language models (LVMs) extend large language models (LLMs) with visual perception capabilities, enabling them to process and interpret visual information. A major challenge compromising their reliability is object hallucination that LVMs may generate plausible but factually inaccurate information. We propose a novel visual adversarial perturbation (VAP) method to mitigate this hallucination issue. VAP alleviates LVM hallucination by applying strategically optimized visual noise without altering the base model. Our method formulates hallucination suppression as an optimization problem, leveraging adversarial strategies to generate beneficial visual perturbations that enhance the model's factual grounding and reduce parametric knowledge bias. Extensive experimental results demonstrate that our method consistently reduces object hallucinations across 8 state-of-the-art LVMs, validating its efficacy across diverse evaluations.
The VAP method generates beneficial visual noise by leveraging adversarial knowledge through the optimization of three strategies: (1) maximizing the semantic alignment between the LVM's response and the visual content to preserve the semantic consistency of the image, (2) minimizing the response similarity between the original and distorted visual content through noise-induced uncertainty, and (3) mitigating parametric knowledge bias by minimizing the similarity of representations between original and distorted inputs. Strategies (2) and (3) jointly mitigate parametric knowledge bias. The optimized visual noise effectively mitigates object hallucinations.
@article{zhang2025poison,
title={Poison as Cure: Visual Noise for Mitigating Object Hallucinations in LVMs},
author={Kejia Zhang and Keda Tao and Jiasheng Tang and Huan Wang},
journal={arXiv preprint arXiv:2501.19164},
year={2025}
}
