Adaptive Median Smoothing: Adversarial Defense for Unlearned Text-to-Image Diffusion Models at Inference Time

Xiaoxuan Han, Songlin Yang, Wei Wang, Yang Li, Jing Dong
2/14/2026

No confident code implementation found for this paper. 5 possible matches can be shown below.

Semantic Scholar

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 5 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

PinNet: Pinpoint Instructive Information for Retrieval Augmented Code-to-Text Generation

94% match2026

3DET-Mamba: Causal Sequence Modelling for End-to-End 3D Object Detection

94% match2026

Würstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models

94% match2026

Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis

94% match1 repo3462026

Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales

93% match2026

BDC-CLIP: Brownian Distance Covariance for Adapting CLIP to Action Recognition

93% match1 repo122026

Cite this paper

@article{han2026adaptive,
  title  = {Adaptive Median Smoothing: Adversarial Defense for Unlearned Text-to-Image Diffusion Models at Inference Time},
  author = {Xiaoxuan Han and Songlin Yang and Wei Wang and Yang Li and Jing Dong},
  year   = {2026},
  url    = {https://api.semanticscholar.org/CorpusID:2d8bb6315b61037a542d4cc98924856129de77be},
  journal = {ICML 2025 2025}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2d8bb6315b61037a542d4cc98924856129de77be.svg)](https://codeofpaper.com/paper/2d8bb6315b61037a542d4cc98924856129de77be)

Discussion