SACE: Concept Erasure at the Semantic Singularity in Visual Autoregressive Models

• URL: http://arxiv.org/abs/2606.15819v1
• Date: Sun, 14 Jun 2026 13:39:00 GMT
• Title: SACE: Concept Erasure at the Semantic Singularity in Visual Autoregressive Models
• Abstract summary: We introduce the first scale-aware concept erasure framework (SACE) for visual autoregressive ( VAR) models.<n>By strictly confining interventions to the first scale, our approach couples an Entropy-Regularized Erasure Objective to prevent high-entropy sampling degeneration.
• Score: 10.865745057624833
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid progress of visual autoregressive (VAR) models has unlocked a transformative frontier for high-fidelity text-to-image synthesis, while heightening concerns over the safety alignment of generated content. Naive application of existing erasure techniques to VAR models causes catastrophic semantic collapse and visual artifacts, since they are predominantly designed for the homogeneous denoising steps of diffusion models. To address this foundational challenge, we first propose the Semantic Singularity Axiom, which posits that any target semantic concept embedded within a prompt is definitively locked at Scale-0. Then rigorously validate this axiom through our proposed Incremental Semantic Saliency Analysis (ISSA),which also enable the community to transparently inspect the coarse-to-fine semantic injection process. Guided by this insight, we introduce the first scale-aware concept erasure framework (SACE) for VAR models. By strictly confining interventions to the first scale, our approach couples an Entropy-Regularized Erasure Objective to prevent high-entropy sampling degeneration, alongside a restorative preservation loss to safely anchor the integrity of entangled benign priors. Extensive experiments demonstrate that our method achieves surgical concept erasure performance across various domains with minimal training overhead, timely and elegently resolute the critical safety vulnerabilities inherent in emerging VAR architectures. Code is available at: https://github.com/limerenceysy/SACE}{https://github.com/limerenceysy/SACE.

Related papers

• EraseSAE: Surgical Concept Erasure in Text-to-Video Diffusion Models via Sparse Autoencoders [103.46709005379591]
  Concept erasure offers a principled remedy by removing unwanted semantics from pretrained models.<n>Existing approaches typically operate at a coarse quality, misaligned with the finegrained, distributed nature of concept representations.<n>We propose EraseSAE, a novel framework that leverages sparse autoencoders to achieve surgical concept erasure.
  arXiv  Detail & Related papers  (2026-09-03T10:23:37Z)
• Uni-AdaVD: Universal Concept Erasure for Visual Generation via Orthogonal Value Decomposition [62.947473058878565]
  We present Uni-AdaVD, a universal inference-time concept erasure framework for visual generation.<n>Our results suggest that Uni-AdaVD provides an efficient and adaptable safety mechanism for modern visual generative models.
  arXiv  Detail & Related papers  (2026-07-16T03:16:51Z)
• FlowErase-RL: Rethinking Concept Erasure as Reward Optimization in Flow Matching Models [47.80885954469943]
  Flow matching models have significantly improved text-to-image generation quality, but also introduce growing safety risks.<n>We propose emphFlowErase-RL, the first GRPO-based framework for concept erasure in flow matching models.<n>We show that our method achieves state-of-the-art erasure performance while maintaining strong image quality and semantic alignment.
  arXiv  Detail & Related papers  (2026-05-19T12:10:09Z)
• 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)
• CGCE: Classifier-Guided Concept Erasure in Generative Models [53.7410000675294]
  Concept erasure has been developed to remove undesirable concepts from pre-trained models.<n>Existing methods remain vulnerable to adversarial attacks that can regenerate the erased content.<n>We introduce an efficient plug-and-play framework that provides robust concept erasure for diverse generative models.
  arXiv  Detail & Related papers  (2025-11-08T05:38:18Z)
• 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)
• 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)
• 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)
• Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models? [52.238883592674696]
  Ring-A-Bell is a model-agnostic red-teaming tool for T2I diffusion models. It identifies problematic prompts for diffusion models with the corresponding generation of inappropriate content. Our results show that Ring-A-Bell, by manipulating safe prompting benchmarks, can transform prompts that were originally regarded as safe to evade existing safety mechanisms.
  arXiv  Detail & Related papers  (2023-10-16T02:11:20Z)

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.