AdvDreamer Unveils: Are Vision-Language Models Truly Ready for Real-World 3D Variations?
Journal:
arXiv
Published Date:
Dec 4, 2024
Abstract
Vision Language Models (VLMs) have exhibited remarkable generalization
capabilities, yet their robustness in dynamic real-world scenarios remains
largely unexplored. To systematically evaluate VLMs' robustness to real-world
3D variations, we propose AdvDreamer, the first framework capable of generating
physically reproducible Adversarial 3D Transformation (Adv-3DT) samples from
single-view observations. In AdvDreamer, we integrate three key innovations:
Firstly, to characterize real-world 3D variations with limited prior knowledge
precisely, we design a zero-shot Monocular Pose Manipulation pipeline built
upon generative 3D priors. Secondly, to ensure the visual quality of worst-case
Adv-3DT samples, we propose a Naturalness Reward Model that provides continuous
naturalness regularization during adversarial optimization, effectively
preventing convergence to hallucinated or unnatural elements. Thirdly, to
enable systematic evaluation across diverse VLM architectures and
visual-language tasks, we introduce the Inverse Semantic Probability loss as
the adversarial optimization objective, which solely operates in the
fundamental visual-textual alignment space. Based on the captured Adv-3DT
samples with high aggressiveness and transferability, we establish MM3DTBench,
the first VQA benchmark dataset tailored to evaluate VLM robustness under
challenging 3D variations. Extensive evaluations of representative VLMs with
varying architectures reveal that real-world 3D variations can pose severe
threats to model performance across various tasks.
