eval-unlearn: Benchmarking unlearning in Text-to-Image Diffusion Models

arXiv:2609.35269 · cs.LG, cs.AI, cs.CV · Submitted 2026-09-28 · Read on arXiv

cs.LG, cs.AI, cs.CV

Submitted: 2026-09-28

Updated: 2026-09-28

Terminology

• Diffusion Model — 22× in this paper · explained in 8 episodes
• Diffusion Models — 18× in this paper · explained in 6 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Adversarial Robustness — 4× in this paper · explained in 3 episodes
• Benchmarking — 4× in this paper · explained in 3 episodes
• CVF International Conference on Computer Vision — 4× in this paper
• Core — 4× in this paper · explained in 2 episodes
• Fidelity — 4× in this paper · explained in 2 episodes
• Machine Unlearning — 4× in this paper · explained in 2 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Accuracy — 1× in this paper · explained in 1 episode
• Adversarial Training — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Attack Success Rate (ASR) — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 1× in this paper
• In International Conference on Machine Learning — 1× in this paper
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Standardized Metrics — 1× in this paper · explained in 3 episodes
• Steering — 1× in this paper · explained in 2 episodes

Sources

• Rethinking the Vulnerability of Concept Erasure and a New Method
• Fast Machine Unlearning Without Retraining Through Selective Synaptic Dampening
• TraSCE: Trajectory Steering for Concept Erasure
• A Comprehensive Survey on Concept Erasure in Text-to-Image Diffusion Models
• Concept Steerers: Leveraging K-Sparse Autoencoders for Test-Time Controllable Generations
• Microsoft COCO: Common Objects in Context
• Holistic Unlearning Benchmark: A Multi-Faceted Evaluation for Text-to-Image Diffusion Model Unlearning
• MMA-Diffusion: MultiModal Attack on Diffusion Models

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