CRCE: Coreference-Retention Concept Erasure in Text-to-Image Diffusion Models

• URL: http://arxiv.org/abs/2503.14232v1
• Date: Tue, 18 Mar 2025 13:09:01 GMT
• Title: CRCE: Coreference-Retention Concept Erasure in Text-to-Image Diffusion Models
• Authors: Yuyang Xue, Edward Moroshko, Feng Chen, Steven McDonagh, Sotirios A. Tsaftaris,
• Abstract summary: We introduce CRCE, a novel concept erasure framework.<n>By explicitly modeling coreferential and retained concepts semantically, CRCE enables more precise concept removal.<n>Experiments demonstrate that CRCE outperforms existing methods on diverse erasure tasks.
• Score: 19.074434401274285
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text-to-Image diffusion models can produce undesirable content that necessitates concept erasure techniques. However, existing methods struggle with under-erasure, leaving residual traces of targeted concepts, or over-erasure, mistakenly eliminating unrelated but visually similar concepts. To address these limitations, we introduce CRCE, a novel concept erasure framework that leverages Large Language Models to identify both semantically related concepts that should be erased alongside the target and distinct concepts that should be preserved. By explicitly modeling coreferential and retained concepts semantically, CRCE enables more precise concept removal, without unintended erasure. Experiments demonstrate that CRCE outperforms existing methods on diverse erasure tasks.

Related papers

• ACE: Attentional Concept Erasure in Diffusion Models [0.0]
  Attentional Concept Erasure integrates a closed-form attention manipulation with lightweight fine-tuning. We show that ACE achieves state-of-the-art concept removal efficacy and robustness. Compared to prior methods, ACE better balances generality (erasing concept and related terms) and specificity (preserving unrelated content)
  arXiv  Detail & Related papers  (2025-04-16T08:16:28Z)
• Fine-Grained Erasure in Text-to-Image Diffusion-based Foundation Models [56.35484513848296]
  FADE (Fine grained Attenuation for Diffusion Erasure) is an adjacency-aware unlearning algorithm for text-to-image generative models. It removes target concepts with minimal impact on correlated concepts, achieving a 12% improvement in retention performance over state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-03-25T15:49:48Z)
• Continual Unlearning for Foundational Text-to-Image Models without Generalization Erosion [56.35484513848296]
  This research introduces continual unlearning', a novel paradigm that enables the targeted removal of multiple specific concepts from foundational generative models. We propose Decremental Unlearning without Generalization Erosion (DUGE) algorithm which selectively unlearns the generation of undesired concepts.
  arXiv  Detail & Related papers  (2025-03-17T23:17:16Z)
• Concept Corrector: Erase concepts on the fly for text-to-image diffusion models [13.065682925662237]
  Concept erasure aims to erase any undesired concepts that the models can generate.<n>We propose Concept Corrector, which checks target concepts based on visual features provided by final generated images predicted at certain time steps.<n>In the whole pipeline, our method changes no model parameters and only requires a given target concept as well as the corresponding replacement content.
  arXiv  Detail & Related papers  (2025-02-22T21:53:43Z)
• Fantastic Targets for Concept Erasure in Diffusion Models and Where To Find Them [21.386640828092524]
  Concept erasure has emerged as a promising technique for mitigating the risk of harmful content generation in diffusion models.<n>We propose the Adaptive Guided Erasure (AGE) method, which emphdynamically selects optimal target concepts tailored to each undesirable concept.<n>Results show that AGE significantly outperforms state-of-the-art erasure methods on preserving unrelated concepts while maintaining effective erasure performance.
  arXiv  Detail & Related papers  (2025-01-31T08:17:23Z)
• Reliable and Efficient Concept Erasure of Text-to-Image Diffusion Models [76.39651111467832]
  We introduce Reliable and Efficient Concept Erasure (RECE), a novel approach that modifies the model in 3 seconds without necessitating additional fine-tuning. To mitigate inappropriate content potentially represented by derived embeddings, RECE aligns them with harmless concepts in cross-attention layers. The derivation and erasure of new representation embeddings are conducted iteratively to achieve a thorough erasure of inappropriate concepts.
  arXiv  Detail & Related papers  (2024-07-17T08:04:28Z)
• Six-CD: Benchmarking Concept Removals for Benign Text-to-image Diffusion Models [58.74606272936636]
  Text-to-image (T2I) diffusion models have shown exceptional capabilities in generating images that closely correspond to textual prompts. The models could be exploited for malicious purposes, such as generating images with violence or nudity, or creating unauthorized portraits of public figures in inappropriate contexts. concept removal methods have been proposed to modify diffusion models to prevent the generation of malicious and unwanted concepts.
  arXiv  Detail & Related papers  (2024-06-21T03:58:44Z)
• Separable Multi-Concept Erasure from Diffusion Models [52.51972530398691]
  We propose a Separable Multi-concept Eraser (SepME) to eliminate unsafe concepts from large-scale diffusion models. The latter separates optimizable model weights, making each weight increment correspond to a specific concept erasure. Extensive experiments indicate the efficacy of our approach in eliminating concepts, preserving model performance, and offering flexibility in the erasure or recovery of various concepts.
  arXiv  Detail & Related papers  (2024-02-03T11:10:57Z)
• Receler: Reliable Concept Erasing of Text-to-Image Diffusion Models via Lightweight Erasers [24.64639078273091]
  Concept erasure in text-to-image diffusion models aims to disable pre-trained diffusion models from generating images related to a target concept. We propose Reliable Concept Erasing via Lightweight Erasers (Receler)
  arXiv  Detail & Related papers  (2023-11-29T15:19:49Z)
• Implicit Concept Removal of Diffusion Models [92.55152501707995]
  Text-to-image (T2I) diffusion models often inadvertently generate unwanted concepts such as watermarks and unsafe images. We present the Geom-Erasing, a novel concept removal method based on the geometric-driven control.
  arXiv  Detail & Related papers  (2023-10-09T17:13:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.