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New TsallisPGD attack method improves adversarial attacks on semantic segmentation models

作者 PulseAugur 编辑部 · [2 个来源] ·

Researchers have developed TsallisPGD, a novel adversarial attack method designed to more effectively target semantic segmentation models. This new approach utilizes Tsallis cross-entropy, a generalized form of standard cross-entropy, to adaptively adjust gradient weighting across pixels. Experiments on datasets like Cityscapes and Pascal VOC demonstrate that TsallisPGD outperforms existing methods in reducing model accuracy and mean intersection over union (mIoU). AI

影响 Introduces a more potent attack vector for evaluating the robustness of semantic segmentation models.

排序理由 This is a research paper detailing a new method for adversarial attacks on semantic segmentation models.

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

New TsallisPGD attack method improves adversarial attacks on semantic segmentation models

报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Alexander Matyasko, Xin Lou, Indriyati Atmosukarto, Wei Zhang ·

    TsallisPGD: Adaptive Gradient Weighting for Adversarial Attacks on Semantic Segmentation

    arXiv:2605.03405v1 Announce Type: new Abstract: Attacking semantic segmentation models is significantly harder than image classification models because an attacker must flip thousands of pixel predictions simultaneously. Standard pixel-wise cross-entropy (CE) is ill-suited to thi…

  2. arXiv cs.CV TIER_1 English(EN) · Wei Zhang ·

    TsallisPGD: Adaptive Gradient Weighting for Adversarial Attacks on Semantic Segmentation

    Attacking semantic segmentation models is significantly harder than image classification models because an attacker must flip thousands of pixel predictions simultaneously. Standard pixel-wise cross-entropy (CE) is ill-suited to this setting: it tends to overemphasize already-mis…

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