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Oct 12 · Monday

All papers

Found 4 papers
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Attack type

Poisoning and backdoors10Extraction and theft3Malicious use1

Defense type

Model hardening3

Impact

Data and privacy leaks3

System type

Models and APIs4

Layer

Prompt layer5Model layer10Application layer4Training and data layer4

System component

Fine-tuning and open weights3Multimodal4
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Attack type：Extraction and theftAttack type：Malicious useLayer：Training and data layerSystem component：MultimodalClear all

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  1. DefensesarXiv 2610.03980

    FADE：面向视频扩散模型的帧感知多概念擦除框架

    提出 FADE，对文生视频模型做帧感知多概念擦除

    arXiv
    2610.03980
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    Wan2.1-T2V-1.3B, Wan2.1-T2V-14B, CogVideoX-2B and others4
    AbstractFADE 用闭式编辑、帧感知适配器和软路由，在单骨干上擦除多概念并压低逐帧残差。

    FADE 是面向文生视频扩散 Transformer 的多概念遗忘方法，用来抑制指定物体、画风、裸体、暴力、仇恨和名人身份，并尽量保持其他生成。作者要处理两件事。与帧无关的抑制会让概念在少数帧重现，片段均值会掩盖这一失败。

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  3. Evaluation and benchmarksarXiv 2609.33481

    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

    arXiv
    2609.33481
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    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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  4. DefensesarXiv 2609.15671

    QPriv-VL：面向视觉语言模型的问题引导 token 裁剪隐私防御

    提出 QPriv-VL，用问题引导剪枝降低分布式视觉问答的隐私泄露

    arXiv
    2609.15671
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    LLaVA-1.5-7B, Llama-2
    AbstractQPriv-VL 在切层前按问题相关度与 DINOv2 敏感度剪视觉 token，作者称能在约四成 token 下兼顾准确率与四类攻击。

    QPriv-VL 把问题引导的视觉 token 剪枝做成分布式 VQA 的客户端隐私防御，而不是只做集中式推理加速。

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