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Oct 12 · Monday

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Found 2 papers
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Defense type

Model hardening2Inference-time intervention3

Impact

Harmful content2

System component

Reasoning chains3Fine-tuning and open weights10Vision3Image generation2Multimodal6
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Defense type：Model hardeningImpact：Harmful contentSystem component：Image generationClear all

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  1. DefensesarXiv 2610.05401

    AVCE：面向扩散模型可验证概念擦除的审计感知遗忘框架

    提出审计感知遗忘框架 AVCE，从文本与视觉通路擦除扩散模型概念

    arXiv
    2610.05401
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Attacker
    White-box, Black-box
    Evaluated models
    Stable Diffusion XL (SDXL), Stable Diffusion v1.5 (SD v1.5), Flux 1.0
    AbstractAVCE 在审计锚点上改两条注意力通路。

    AVCE 是面向扩散模型的概念擦除方法，要让擦除经得起绕过文本条件的潜空间审计，而不只是挡住对抗提示词。

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  2. 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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