Concept Replacer: Replacing Sensitive Concepts in Diffusion Models via Precision Localization
Fuente:
arXiv
Saved in:
| Main Authors: | Zhang, Lingyun, Xie, Yu, Fu, Yanwei, Chen, Ping |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
SafeCtrl: Region-Based Safety Control for Text-to-Image Diffusion via Detect-Then-Suppress
by: Zhang, Lingyun, et al.
Published: (2025)
by: Zhang, Lingyun, et al.
Published: (2025)
Do Concept Replacement Techniques Really Erase Unacceptable Concepts?
by: Das, Anudeep, et al.
Published: (2025)
by: Das, Anudeep, et al.
Published: (2025)
Where Concept Erasure Should Occur: Concept-Layer Alignment in Text-to-Video Diffusion Models
by: Xie, Yiwei, et al.
Published: (2026)
by: Xie, Yiwei, et al.
Published: (2026)
Replace in Translation: Boost Concept Alignment in Counterfactual Text-to-Image
by: Li, Sifan, et al.
Published: (2025)
by: Li, Sifan, et al.
Published: (2025)
PROBE: Diagnosing Residual Concept Capacity in Erased Text-to-Video Diffusion Models
by: Xie, Yiwei, et al.
Published: (2026)
by: Xie, Yiwei, et al.
Published: (2026)
NSFW-Classifier Guided Prompt Sanitization for Safe Text-to-Image Generation
by: Xie, Yu, et al.
Published: (2025)
by: Xie, Yu, et al.
Published: (2025)
Erasing Concepts, Steering Generations: A Comprehensive Survey of Concept Suppression
by: Xie, Yiwei, et al.
Published: (2025)
by: Xie, Yiwei, et al.
Published: (2025)
SafeCtrl: Region-Aware Safety Control for Text-to-Image Diffusion via Detect-Then-Suppress
by: Zhang, Lingyun, et al.
Published: (2026)
by: Zhang, Lingyun, et al.
Published: (2026)
SPEED: Scalable, Precise, and Efficient Concept Erasure for Diffusion Models
by: Li, Ouxiang, et al.
Published: (2025)
by: Li, Ouxiang, et al.
Published: (2025)
Explain via Any Concept: Concept Bottleneck Model with Open Vocabulary Concepts
by: Tan, Andong, et al.
Published: (2024)
by: Tan, Andong, et al.
Published: (2024)
FaR: Enhancing Multi-Concept Text-to-Image Diffusion via Concept Fusion and Localized Refinement
by: Tran, Gia-Nghia, et al.
Published: (2025)
by: Tran, Gia-Nghia, et al.
Published: (2025)
Implicit Concept Removal of Diffusion Models
by: Liu, Zhili, et al.
Published: (2023)
by: Liu, Zhili, et al.
Published: (2023)
Replace Anyone in Videos
by: Wang, Xiang, et al.
Published: (2024)
by: Wang, Xiang, et al.
Published: (2024)
ConceptPrism: Concept Disentanglement in Personalized Diffusion Models via Residual Token Optimization
by: Kim, Minseo, et al.
Published: (2026)
by: Kim, Minseo, et al.
Published: (2026)
Enhancing Concept Localization in CLIP-based Concept Bottleneck Models
by: Kazmierczak, Rémi, et al.
Published: (2025)
by: Kazmierczak, Rémi, et al.
Published: (2025)
Unlearning Concepts in Diffusion Model via Concept Domain Correction and Concept Preserving Gradient
by: Wu, Yongliang, et al.
Published: (2024)
by: Wu, Yongliang, et al.
Published: (2024)
CusConcept: Customized Visual Concept Decomposition with Diffusion Models
by: Xu, Zhi, et al.
Published: (2024)
by: Xu, Zhi, et al.
Published: (2024)
Locality-aware Concept Bottleneck Model
by: Jeon, Sujin, et al.
Published: (2025)
by: Jeon, Sujin, et al.
Published: (2025)
Reliable and Efficient Concept Erasure of Text-to-Image Diffusion Models
by: Gong, Chao, et al.
Published: (2024)
by: Gong, Chao, et al.
Published: (2024)
A Single Neuron Works: Precise Concept Erasure in Text-to-Image Diffusion Models
by: He, Qinqin, et al.
Published: (2025)
by: He, Qinqin, et al.
Published: (2025)
Robust Concept Erasure in Diffusion Models: A Theoretical Perspective on Security and Robustness
by: Fu, Zixuan, et al.
Published: (2025)
by: Fu, Zixuan, et al.
Published: (2025)
Non-confusing Generation of Customized Concepts in Diffusion Models
by: Lin, Wang, et al.
Published: (2024)
by: Lin, Wang, et al.
Published: (2024)
Comprehensive Evaluation and Analysis for NSFW Concept Erasure in Text-to-Image Diffusion Models
by: Chen, Die, et al.
Published: (2025)
by: Chen, Die, et al.
Published: (2025)
Mass Concept Erasure in Diffusion Models with Concept Hierarchy
by: Tu, Jiahang, et al.
Published: (2026)
by: Tu, Jiahang, et al.
Published: (2026)
On the Concept Trustworthiness in Concept Bottleneck Models
by: Huang, Qihan, et al.
Published: (2024)
by: Huang, Qihan, et al.
Published: (2024)
Continuous Concepts Removal in Text-to-image Diffusion Models
by: Han, Tingxu, et al.
Published: (2024)
by: Han, Tingxu, et al.
Published: (2024)
DuMo: Dual Encoder Modulation Network for Precise Concept Erasure
by: Han, Feng, et al.
Published: (2025)
by: Han, Feng, et al.
Published: (2025)
ConceptSplit: Decoupled Multi-Concept Personalization of Diffusion Models via Token-wise Adaptation and Attention Disentanglement
by: Lim, Habin, et al.
Published: (2025)
by: Lim, Habin, et al.
Published: (2025)
ConceptMaster: Multi-Concept Video Customization on Diffusion Transformer Models Without Test-Time Tuning
by: Huang, Yuzhou, et al.
Published: (2025)
by: Huang, Yuzhou, et al.
Published: (2025)
Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models
by: Xie, Yan, et al.
Published: (2025)
by: Xie, Yan, et al.
Published: (2025)
Rethinking Robust Adversarial Concept Erasure in Diffusion Models
by: Yin, Qinghong, et al.
Published: (2025)
by: Yin, Qinghong, et al.
Published: (2025)
Concept Unlearning by Modeling Key Steps of Diffusion Process
by: Zhang, Chaoshuo, et al.
Published: (2025)
by: Zhang, Chaoshuo, et al.
Published: (2025)
FADE: Adversarial Concept Erasure in Flow Models
by: Fu, Zixuan, et al.
Published: (2025)
by: Fu, Zixuan, et al.
Published: (2025)
Concept-wise Attention for Fine-grained Concept Bottleneck Models
by: Zhong, Minghong, et al.
Published: (2026)
by: Zhong, Minghong, et al.
Published: (2026)
Forget Many, Forget Right: Scalable and Precise Concept Unlearning in Diffusion Models
by: Deng, Kaiyuan, et al.
Published: (2026)
by: Deng, Kaiyuan, et al.
Published: (2026)
Growth Inhibitors for Suppressing Inappropriate Image Concepts in Diffusion Models
by: Chen, Die, et al.
Published: (2024)
by: Chen, Die, et al.
Published: (2024)
A Comprehensive Survey on Visual Concept Mining in Text-to-image Diffusion Models
by: Li, Ziqiang, et al.
Published: (2025)
by: Li, Ziqiang, et al.
Published: (2025)
CURE: Concept Unlearning via Orthogonal Representation Editing in Diffusion Models
by: Biswas, Shristi Das, et al.
Published: (2025)
by: Biswas, Shristi Das, et al.
Published: (2025)
Temporal Concept Dynamics in Diffusion Models via Prompt-Conditioned Interventions
by: Gorgun, Ada, et al.
Published: (2025)
by: Gorgun, Ada, et al.
Published: (2025)
Pruning for Robust Concept Erasing in Diffusion Models
by: Yang, Tianyun, et al.
Published: (2024)
by: Yang, Tianyun, et al.
Published: (2024)
Similar Items
-
SafeCtrl: Region-Based Safety Control for Text-to-Image Diffusion via Detect-Then-Suppress
by: Zhang, Lingyun, et al.
Published: (2025) -
Do Concept Replacement Techniques Really Erase Unacceptable Concepts?
by: Das, Anudeep, et al.
Published: (2025) -
Where Concept Erasure Should Occur: Concept-Layer Alignment in Text-to-Video Diffusion Models
by: Xie, Yiwei, et al.
Published: (2026) -
Replace in Translation: Boost Concept Alignment in Counterfactual Text-to-Image
by: Li, Sifan, et al.
Published: (2025) -
PROBE: Diagnosing Residual Concept Capacity in Erased Text-to-Video Diffusion Models
by: Xie, Yiwei, et al.
Published: (2026)