English(EN) Universal Cross-Prompt Adversarial Attacks on Promptable Concept Segmentation

新的攻击方法针对先进的AI分割模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-01 04:00

研究人员开发了AdvPCS，一种新颖的对抗性攻击方法，旨在破坏可提示概念分割模型，包括最新的SAM3。这种攻击技术侧重于通用的跨提示可迁移性，意味着单一的对抗性扰动可以影响多个提示甚至不同的视频。AdvPCS采用最小-最大提示优化和全局-局部感知欺骗等策略，显著降低模型性能，在某些数据集上将平均mIoU降低到5%以下。 AI

影响这项研究突显了先进AI分割模型的潜在漏洞，需要进一步加强鲁棒性和安全性。

排序理由该集群包含一篇研究论文，详细介绍了一种针对AI模型的新型对抗性攻击方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Ziqi Zhou, Yifan Hu, Yufei Song, Haowen Jiang, Xianlong Wang, Shengshan Hu, Dezhong Yao, Leo Yu Zhang · 2026-10-01 04:00

Promptable Concept Segmentation 的通用跨提示对抗性攻击

arXiv:2609.39265v1 Announce Type: new Abstract: The Segment Anything Model (SAM) achieves remarkable performance in visual segmentation. The latest SAM3 extends promptable segmentation to concept-level prediction, broadening the scope of segmentation foundation models. While rece…

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Promptable Concept Segmentation 的通用跨提示对抗性攻击

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