Semantic Surgery: Zero-Shot Concept Erasure in Diffusion Models

• URL: http://arxiv.org/abs/2510.22851v1
• Date: Sun, 26 Oct 2025 22:04:17 GMT
• Title: Semantic Surgery: Zero-Shot Concept Erasure in Diffusion Models
• Authors: Lexiang Xiong, Chengyu Liu, Jingwen Ye, Yan Liu, Yuecong Xu,
• Abstract summary: We introduce a novel training-free, zero-shot framework for concept erasure that operates directly on text embeddings before the diffusion process.<n>We achieve superior completeness and robustness while preserving locality and image quality.<n>This robustness also allows our framework to function as a built-in threat detection system, offering a practical solution for safer text-to-image generation.
• Score: 27.672305802461377
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Concept erasure in text-to-image diffusion models is crucial for mitigating harmful content, yet existing methods often compromise generative quality. We introduce Semantic Surgery, a novel training-free, zero-shot framework for concept erasure that operates directly on text embeddings before the diffusion process. It dynamically estimates the presence of target concepts in a prompt and performs a calibrated vector subtraction to neutralize their influence at the source, enhancing both erasure completeness and locality. The framework includes a Co-Occurrence Encoding module for robust multi-concept erasure and a visual feedback loop to address latent concept persistence. As a training-free method, Semantic Surgery adapts dynamically to each prompt, ensuring precise interventions. Extensive experiments on object, explicit content, artistic style, and multi-celebrity erasure tasks show our method significantly outperforms state-of-the-art approaches. We achieve superior completeness and robustness while preserving locality and image quality (e.g., 93.58 H-score in object erasure, reducing explicit content to just 1 instance, and 8.09 H_a in style erasure with no quality degradation). This robustness also allows our framework to function as a built-in threat detection system, offering a practical solution for safer text-to-image generation.

Related papers

• SafeRedir: Prompt Embedding Redirection for Robust Unlearning in Image Generation Models [67.84174763413178]
  We introduce SafeRedir, a lightweight inference-time framework for robust unlearning via prompt embedding redirection.<n>We show that SafeRedir achieves effective unlearning capability, high semantic and perceptual preservation, robust image quality, and enhanced resistance to adversarial attacks.
  arXiv  Detail & Related papers  (2026-01-13T15:01:38Z)
• Closing the Safety Gap: Surgical Concept Erasure in Visual Autoregressive Models [48.34555526275907]
  We propose a novel framework VARE that enables stable concept erasure in visual autoregressive models.<n>We then introduce S-VARE, a novel and effective concept erasure method designed for VAR.<n>Our approach achieves surgical concept erasure while preserving generation quality, thereby closing the safety gap in autoregressive text-to-image generation.
  arXiv  Detail & Related papers  (2025-09-26T14:26:52Z)
• VCE: Safe Autoregressive Image Generation via Visual Contrast Exploitation [57.36681904639463]
  Methods to safeguard autoregressive text-to-image models remain underexplored.<n>We propose Visual Contrast Exploitation (VCE), a novel framework that precisely decouples unsafe concepts from their associated content semantics.<n>Our experiments demonstrate that our method effectively secures the model, achieving state-of-the-art results while erasing unsafe concepts and maintaining the integrity of unrelated safe concepts.
  arXiv  Detail & Related papers  (2025-09-21T09:00:27Z)
• Robust Concept Erasure in Diffusion Models: A Theoretical Perspective on Security and Robustness [4.23067546195708]
  textbfSCORE (Secure and Concept-Oriented Robust Erasure) is a novel framework for robust concept removal in diffusion models.<n>SCORE sets a new standard for secure and robust concept erasure in diffusion models.
  arXiv  Detail & Related papers  (2025-09-15T15:05:50Z)
• TRACE: Trajectory-Constrained Concept Erasure in Diffusion Models [0.0]
  Concept erasure aims to remove or suppress specific concept information in a generative model.<n>Trajectory-Constrained Attentional Concept Erasure (TRACE) is a novel method to erase targeted concepts from diffusion models.<n>TRACE achieves state-of-the-art performance, outperforming recent methods such as ANT, EraseAnything, and MACE in terms of removal efficacy and output quality.
  arXiv  Detail & Related papers  (2025-05-29T10:15:22Z)
• CURE: Concept Unlearning via Orthogonal Representation Editing in Diffusion Models [7.68494752148263]
  CURE is a training-free concept unlearning framework that operates directly in the weight space of pre-trained diffusion models.<n>The Spectral Eraser identifies and isolates features unique to the undesired concept while preserving safe attributes.<n>CURE achieves a more efficient and thorough removal for targeted artistic styles, objects, identities, or explicit content.
  arXiv  Detail & Related papers  (2025-05-19T03:53:06Z)
• One Image is Worth a Thousand Words: A Usability Preservable Text-Image Collaborative Erasing Framework [127.07102988701092]
  We introduce the first text-image Collaborative Concept Erasing (Co-Erasing) framework.<n>Co-Erasing describes the concept jointly by text prompts and the corresponding undesirable images induced by the prompts.<n>We design a text-guided image concept refinement strategy that directs the model to focus on visual features most relevant to the specified text concept.
  arXiv  Detail & Related papers  (2025-05-16T11:25:50Z)
• TRCE: Towards Reliable Malicious Concept Erasure in Text-to-Image Diffusion Models [53.937498564603054]
  Recent advances in text-to-image diffusion models enable photorealistic image generation, but they also risk producing malicious content, such as NSFW images.<n>To mitigate risk, concept erasure methods are studied to facilitate the model to unlearn specific concepts.<n>We propose TRCE, using a two-stage concept erasure strategy to achieve an effective trade-off between reliable erasure and knowledge preservation.
  arXiv  Detail & Related papers  (2025-03-10T14:37:53Z)
• 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)
• Unlearning Concepts in Diffusion Model via Concept Domain Correction and Concept Preserving Gradient [20.698305103879232]
  We propose a novel concept domain correction framework named textbfDoCo (textbfDomaintextbfCorrection)<n>By aligning the output domains of sensitive and anchor concepts through adversarial training, our approach ensures comprehensive unlearning of target concepts.<n>We also introduce a concept-preserving gradient surgery technique that mitigates conflicting gradient components, thereby preserving the model's utility while unlearning specific concepts.
  arXiv  Detail & Related papers  (2024-05-24T07:47:36Z)

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.