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Oct 12 · Monday

All papers

Found 2 papers
Filter3

Defense type

Model hardening2Inference-time intervention4Detection and filtering9

Attack surface / entry point

Images, audio and video2Training data3

System component

Vision2Multimodal4
Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 265papers have no tags yet and are not included in filtered results.
Defense type：Model hardeningAttack surface / entry point：Images, audio and videoSystem component：VisionClear all

Another 265papers do not have category tags yet and are not included in filtered results.

  1. 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。

    Paper analysisRead analysis
  2. DefensesarXiv 2602.12461

    Semantic-aware adversarial fine-tuning for CLIP

    提出 SAFT，用语义描述集成构造对抗样本并微调图像编码器

    arXiv
    2602.12461
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    ViT-B/32, CLIP-L/14
    AbstractSAFT 用精炼类描述的集成对抗样本微调 CLIP 图像编码器，以改善零样本准确率与鲁棒性的权衡。

    SAFT 是对 CLIP 图像编码器做对抗微调的方法，要改善零样本分类上的准确率–鲁棒性权衡。

    Paper analysisRead analysis
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