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Receler: Reliable Concept Erasing of Text-to-Image Diffusion Models via Lightweight Erasers

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arxiv 2311.17717 v3 pith:2YBTOFVS submitted 2023-11-29 cs.CV cs.LG

Receler: Reliable Concept Erasing of Text-to-Image Diffusion Models via Lightweight Erasers

Chi-Pin Huang , Kai-Po Chang , Chung-Ting Tsai , Yung-Hsuan Lai , Fu-En Yang , Yu-Chiang Frank Wang This is my paper
classification cs.CV cs.LG
keywords conceptdiffusionerasingimageslightweightmodelsrecelerreliable
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Concept erasure in text-to-image diffusion models aims to disable pre-trained diffusion models from generating images related to a target concept. To perform reliable concept erasure, the properties of robustness and locality are desirable. The former refrains the model from producing images associated with the target concept for any paraphrased or learned prompts, while the latter preserves its ability in generating images with non-target concepts. In this paper, we propose Reliable Concept Erasing via Lightweight Erasers (Receler). It learns a lightweight Eraser to perform concept erasing while satisfying the above desirable properties through the proposed concept-localized regularization and adversarial prompt learning scheme. Experiments with various concepts verify the superiority of Receler over previous methods.

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Cited by 4 Pith papers

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  1. Concept Removal for Frontier Image Generative Models

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    A transcoder-based in-place replacement of the bottleneck layer enables selective concept removal in modern diffusion and autoregressive image models without degrading output quality.

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    Gaussian probing infers harmful model specialization from parameter perturbations and internal representation responses to Gaussian latent ensembles rather than from generated outputs.

  3. Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks

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    A system-first taxonomy and literature synthesis of multimodal unlearning across vision, language, video, and audio, with datasets, benchmarks, metrics, applications, and open challenges.

  4. CoreUnlearn: Rethinking Concept Unlearning through Disentangled Component-Level Erasure in Text-guided Diffusion Models

    cs.CR 2026-06 unverdicted novelty 4.0

    CoreUnlearn uses a Component Extraction Module and Swap Disentangling Strategy to remove only erasure-critical components from concept embeddings in diffusion models.