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New framework exposes vulnerabilities in visible-infrared vision-language models

By PulseAugur Editorial · [2 sources] ·

Researchers have developed CFGPatch, a novel adversarial framework designed to expose vulnerabilities in visible-infrared vision-language models (VLMs). This method utilizes curved-edge fractal geometry and a modality-specific rendering mechanism to create adversarial patches that disrupt both shape and texture perception in VLMs. Experiments demonstrate that CFGPatch effectively fools these models and shows strong transferability across different tasks like image captioning and visual question answering. AI

IMPACT This research highlights potential security risks in multimodal AI systems operating in challenging environments, suggesting a need for more robust adversarial defenses.

RANK_REASON The cluster contains an academic paper detailing a new adversarial framework for computer vision models.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New framework exposes vulnerabilities in visible-infrared vision-language models

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The cluster contains an academic paper detailing a new adversarial framework for computer vision models.
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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Xiang Chen, Yuxian Dong, Chao Li, Chengyin Hu, Jiaju Han, Fengyu Zhang, Yiwei Wei, Jiahuan Long, Jiujiang Guo ·

    Exposing Vulnerabilities in Visible-Infrared VLMs: A Unified Geometric Adversarial Framework with Cross-Task Transferability

    arXiv:2605.22273v1 Announce Type: new Abstract: Vision-language models (VLMs) have achieved strong performance across diverse multimodal tasks, but their adversarial robustness in visible-infrared (VIS-IR) scenarios remains underexplored. This gap is critical because VIS-IR sensi…

  2. arXiv cs.CV TIER_1 English(EN) · Jiujiang Guo ·

    Exposing Vulnerabilities in Visible-Infrared VLMs: A Unified Geometric Adversarial Framework with Cross-Task Transferability

    Vision-language models (VLMs) have achieved strong performance across diverse multimodal tasks, but their adversarial robustness in visible-infrared (VIS-IR) scenarios remains underexplored. This gap is critical because VIS-IR sensing is widely used in real-world perception syste…

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