English(EN) IGME: Efficient Chained Method Ensemble for Transferable Semantic Segmentation Attacks

新方法增强了对语义分割模型的对抗性攻击

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-31 04:00

研究人员开发了IGME，一种为语义分割模型生成可迁移对抗性扰动的有效方法。该方法使用单个源模型来组合攻击组件，共享梯度计算以降低成本。IGME采用集成梯度风格的路径平均方向来稳定更新，并在CNN和Transformer模型上展示了与现有方法相比具有竞争力的可迁移性和运行时效率。 AI

影响这项研究可能有助于提高计算机视觉应用中对抗性攻击的鲁棒性防御。

排序理由该条目是一篇学术论文，详细介绍了一种针对计算机视觉模型的新型对抗攻击方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CV TIER_1 English(EN) · Mengqi He, Jing Zhang · 2026-07-31 04:00

IGME：可迁移语义分割攻击的高效链式方法集成

arXiv:2607.27465v1 Announce Type: new Abstract: Semantic segmentation models are vulnerable to transferable adversarial perturbations, yet evaluating transfer attacks on dense prediction models can be computationally expensive. Existing ensemble attacks often rely on multiple sur…