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arXiv Computer Vision By Daizong Liu, Junhao Dong, Zhiyuan Ma, Xiaoye Qu, Xiang Fang, Runwei Guan, Keke Tang, Jianfeng Dong, Yew-Soon Ong

Forbid Your Attention: Fooling Multimodal Large Language Models by Selectively Removing Intrinsic Focus in Spectral Domain

Read the original on arXiv Computer Vision →

The paper introduces a new adversarial attack on multimodal large language models (MLLMs) that targets the models’ intrinsic focus in the frequency domain. By exploiting the models’ sensitivity to phase information, the authors design a phase‑aware perturbation strategy that restricts changes to structure‑relevant phase regions, making attacks both effective and imperceptible. An auxiliary adversarial prompt module further misaligns multimodal attention toward targeted structural patterns, and experiments on several MLLM models and datasets confirm the method’s superior performance over existing attacks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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