The paper introduces Dynamic Semantic Steering (DSS), a training‑free, inference‑time defense for robust concept erasure in text‑to‑image diffusion models. DSS models local semantic neighborhoods geometrically, automatically identifies benign semantic anchors, and applies context‑aware, constrained feature correction using cross‑attention signals. Experiments show DSS achieves an average erasure rate of 91.0%, outperforming prior defenses while reducing semantic drift and preserving generation fidelity.
By Qinghui Gong, Zhengchun Zhou, Hua Meng, Yihuai Liang, Yuxuan Zhang
arXiv:2609.01433v1 Announce Type: new
Abstract: Concept erasure aims to suppress unsafe, privacy-sensitive, or undesirable generations in text-to-image diffusion models while preserving benign semant...
By Qinghui Gong, Xunlei Chen, Yu-Xuan Zhang, Hua Meng, Zhengchun Zhou
arXiv:2607. 23492v1 Announce Type: cross Abstract: Concept erasure techniques (CETs) edit text-to-image diffusion models to erase undesired targets such as NSFW content or copyrighted styles, while preserving model utility on benign concepts.
By Shaswati Saha, Rajasekhar Anguluri, Manas Gaur
Concept Acupuncturist (ConceptACP) is a machine‑unlearning framework that removes targeted concepts from diffusion models efficiently and precisely. It identifies the most relevant cross‑attention block in the U‑Net denoiser, fine‑tunes only that block, and uses Sparse Autoencoders to generate semantically meaningful correction signals for concept erasure. Applied to SDXL‑Turbo, ConceptACP boosts forgetting success by over 36 percentage points while maintaining image quality and prompt fidelity, and works across other Stable Diffusion backbones.
By Yiran Xiahou, Siliang Qin, Kai Chen, Shengzhi Zhang, Renyang Liu
arXiv:2511. 05865v3 Announce Type: replace-cross Abstract: Recent advancements in large-scale generative models have enabled the creation of high-quality images and videos, but have also raised significant safety concerns regarding the generation of unsafe content.
By Viet Nguyen, Vishal M. Patel
HyperErase introduces a hypernetwork-based framework for concept erasure in text-to-image models, replacing static adapters with prompt-conditioned parameter synthesis. The method maps textual descriptions to LoRA updates, eliminating per-prompt gradient optimization and manual merging. A decoupled rectification strategy further stabilizes and refines the synthesized adapters, yielding improved erasure effectiveness, image quality, and semantic alignment across diverse concepts.
By Yi Sun, Xinhao Zhong, Zhiqi Zhang, Yimin Zhou, Junhao Li, Yuxia Qiao
arXiv:2609.37537v1 Announce Type: new
Abstract: Machine unlearning has emerged as a critical post-hoc safety measure to erase sensitive concepts from Text-to-Image (T2I) models without prohibitive re...
By Arian Komaei Koma, Seyed Amir Kasaei, Aida Aryafar, Matin Ghiasi, Ali Aghayari, Amirhossein Souri, Mohammad Mosayyebi, AmirMahdi Sadeghzadeh, Mohammad Hossein Rohban
The paper introduces Hierarchically Grounded Semantic Surgery (HGSS), a training‑free method for removing specific concepts from text‑to‑image diffusion models without altering model weights. HGSS uses hierarchical span grounding to accurately identify target spans and dynamic attribute binding to refine text conditioning during early denoising, thereby preventing routing failures on compositional prompts. Experiments on SEE and UnlearnCanvas show that HGSS significantly reduces hierarchical evasion and attribute leakage, outperforming existing erasure techniques.
By Chen Dai, Ganyu Zou, Nathan Self, Kevin Piper, Ramachandra Rao Seethiraju, Karthik Shyamsunder, Chang-Tien Lu, Naren Ramakrishnan
Training-free concept erasure is an attractive mechanism for controlling text-to-image diffusion models, but precise erasure often comes at the cost of damaging semantically related non-target concepts. Existing value-space methods remove the component of each cross-attention value along the target concept direction, implicitly treating target identity and shared visual structure as the same signal.
RASteer is a training‑free method for concept erasure in text‑to‑image diffusion models that retains other concepts. It constructs a retain subspace from concepts to preserve, then removes components aligned with this subspace from the erasure direction using Retain‑Orthogonal Steering (ROS). Overlap‑Adaptive Calibration (OAC) further adjusts the removal of shared components at each layer and denoising step, balancing target erasure with concept preservation. Experiments show RASteer matches or outperforms existing activation steering and weight editing baselines on unsafe‑content, instance, and artistic‑style erasure tasks across multiple backbones and benchmarks.
By Yongliang Wu, Haori Lu, Yulun Wu, Jinqi Luo, Xingyu Zhu, Yaoyao Liu
arXiv:2606. 15819v1 Announce Type: cross 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.
By Siya Yang, Nanxiang Jiang, Zhaoxin Fan, Yunfeng Diao
The paper introduces AVCE, a method for verifiable concept erasure in diffusion models that audits latent representations to identify vulnerable directions and then edits cross‑attention and self‑attention projections in closed form. By fine‑tuning with pathway‑level auditing losses and orthogonal gradient projection, AVCE reduces attack success rates by 5.07× and improves auditing scores by 3.84× compared to the best baseline while maintaining generation quality.
By Kaiyuan Deng, Yuchen Li, Gen Li, Yang Xiao, Geng Yuan, Xiaoyong Yuan, Bo Hui, Xiaolong Ma