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Oct 13 · Tuesday

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  1. 研究揭示训练效率与模型脆弱性的权衡:高效训练更易受对抗与隐私攻击

    比较高效与高成本训练的对抗和隐私脆弱性并分析损失几何

    Published
    Evaluated models
    ResNet-18, Swin Transformer, Qwen2.5-Math-7B-Instruct and others7
    Abstract效用匹配下,三种高效训练都伴随更高的隐私与对抗脆弱性及更尖的损失几何。

    作者在视觉分类和数学推理语言模型上,检查用更少数据或更简对齐换取训练效率时,安全性质是否一起变化。比较在任务效用接近的前提下进行。视觉侧用 KNN-Shapley 的高重要性小子集对照低重要性大子集,并用预训练微调对照从头训练。

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  2. IDUnlearn-Bench:面向视觉语言模型的个体级遗忘基准

    提出 IDUnlearn-Bench,评测 VLM 个体级遗忘后的身份残留

    Published
    Evaluated models
    Qwen3-VL-2B, Qwen3-VL-4B, Qwen3-VL-8B and others6
    AbstractIDUnlearn-Bench 用四族探针显示:压低直接属性访问后,身份仍常能被反向查出、绑定或重构。

    IDUnlearn-Bench 评测 VLM 是否忘掉一个可跨模态识别的人,而不只是答不出某条监督事实。

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  3. DefensesarXiv 2603.01544

    RA-Det:利用鲁棒性不对称检测 AI 生成图像

    提出 RA-Det,利用鲁棒性不对称检测 AI 生成图像

    Published
    AbstractRA-Det 把扰动下特征漂移差变成检测信号,平均 ACC 高于表中通用检测器,但不是每行最高。

    RA-Det 是合成图像检测器,用扰动下的表示稳定性差异代替外观痕迹。

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