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Oct 12 · Monday

All papers

Found 9 papers

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Defense type

Model hardening4

Impact

Harmful content3

System type

Models and APIs9

Attack surface / entry point

Weights and gradients3

Layer

Prompt layer18 Model layer20 Training and data layer9

System component

Guardrails and judges20 Monitors6 Reasoning chains16 Fine-tuning and open weights46 Vision16 Audio6 Image generation9 Multimodal32

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AttacksarXiv 2610.05453Oct 5
研究者提出 Privacy-Leaking Watermarks，可在本地多模态模型中泄露对话隐私
提出 PLW，用微调把先前对话敏感触发编码进后续生成图像
arXiv
2610.05453
Published
Oct 5
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
OmniGen2, BAGEL-7B
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentFine-tuning and open weights
AbstractPLW 把先前对话线索写成后续图像中的隐水印，检出随触发、模型和间隔变化。
Paper analysisRead analysis
DefensesarXiv 2610.05401Oct 5
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
Oct 5
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
- DefensesModel hardening
- AttacksJailbreaks
- ComponentImage generation
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。
Paper analysisRead analysis
DefensesarXiv 2610.03980Oct 3
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
Oct 3
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Wan2.1-T2V-1.3B, Wan2.1-T2V-14B, CogVideoX-2B and others4
- DefensesModel hardening
- AttacksMalicious use
- ComponentImage generation
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。
Paper analysisRead analysis
DefensesarXiv 2610.02967Oct 2
用偏好奖励与评分标准奖励组合后训练前沿文生图模型
组合偏好奖励与评分标准奖励后训练文生图模型以抑制奖励作弊
arXiv
2610.02967
Published
Oct 2
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
FLUX.2-dev, Ideogram-4
- DefensesModel hardening
- RiskReward hacking
- ComponentImage generation
Abstract组合奖励后训练 FLUX.2-dev 与 Ideogram-4，并报告高于基座的胜率与 Elo。
作者给出一套开放域文生图后训练配方：把人类偏好奖励和 prompt 条件的 rubric 奖励合在一起，而不是换一个新优化器。问题是单一奖励盖不住用户意图。偏好模型能看整体观感，但对“是否实现每个要求”这类稀疏属性不敏感。
Paper analysisRead analysis
OtherarXiv 2610.00812Oct 1
视频生成模型的后训练与对齐综述
综述视频生成的后训练与对齐，并汇总基准与开放挑战
arXiv
2610.00812
Published
Oct 1
Layer
Training and data layer
Setting
Models and APIs
- ComponentImage generation
Abstract综述把视频后训练分成四类对齐信号，并指出奖励、长视频、权衡和安全仍开放。
这是一篇视频生成 后训练与对齐 综述：预训练模型已有强先验，但作者认为仍不能可靠遵循意图、维持时间连贯或满足物理与安全约束。
Paper analysisRead analysis
AttacksarXiv 2609.39047Sep 30
BadAction：通过动作引导触发器对交互式视频生成模型实施后门攻击
提出 BadAction，通过动作触发器给交互式视频生成植入后门
arXiv
2609.39047
Published
Sep 30
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Astra (Wan2.1-T2V-1.3B)
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentFine-tuning and open weights
Paper analysisRead analysis
DefensesarXiv 2609.36099Sep 29
通过逆向蒸馏实现数据遗忘（IDU）
提出 IDU，在无保留样本时蒸馏单步生成器并抑制遗忘子集
arXiv
2609.36099
Published
Sep 29
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
IDU one-step generator
- DefensesModel hardening
- ComponentImage generation
AbstractIDU 同时做单步蒸馏和数据遗忘，在 MNIST 与 CIFAR-10 上压低遗忘类生成率。
IDU 面向无条件 flow 与 score-based 教师，在蒸馏成单步生成器的同时做数据遗忘。
Paper analysisRead analysis
AttacksarXiv 2609.33445Sep 27
CSR：跨架构统一攻击概念擦除
提出跨架构攻击 CSR，用概念分数参数重学习恢复文生图被擦除概念
arXiv
2609.33445
Published
Sep 27
Layer
Training and data layer
Setting
Models and APIs
Attacker
White-box
Evaluated models
FLUX.1-dev, Stable Diffusion-v1.4
- AttacksModel tampering
- ComponentImage generation
Paper analysisRead analysis
OtherarXiv 2602.12155Feb 13
FAIL: Flow Matching Adversarial Imitation Learning for Image Generation
提出 FAIL 对抗模仿学习，对齐图像生成分布与专家演示
arXiv
2602.12155
Published
Feb 13
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
FLUX.1-dev, Xomni, Wan2.1-1.3B
- ComponentImage generation
AbstractFAIL 以对抗模仿做后训练。
Paper analysisRead analysis

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