Anti-Diffusion: Preventing Abuse of Modifications of Diffusion-Based Models
Authors: Li Zheng, Liangbin Xie, Jiantao Zhou, Xintao Wang, Haiwei Wu, Jinyu Tian
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that our Anti-Diffusion achieves superior defense performance across a wide range of diffusion-based techniques in different scenarios. Based on both quantitative and qualitative results, our proposed method, Anti-Diffusion, achieves superior defense effects across several diffusion-based techniques, including tuning methods (such as Dream Booth/Lo RA) and editing methods (such as Masa Ctrl/Diff Edit). |
| Researcher Affiliation | Collaboration | 1University of Macau 2Shenzhen Institute of Advanced Technology 3Kuaishou Technology 4Macau University of Science and Technology |
| Pseudocode | No | The paper describes the overall framework and methodology, including problem definition, prompt tuning strategy, adversarial noise optimization, and UNet update. These are explained using text and a diagram (Figure 2), but there is no explicitly labeled 'Pseudocode' or 'Algorithm' block with structured steps. |
| Open Source Code | Yes | Code https://github.com/whulizheng/Anti-Diffusion |
| Open Datasets | Yes | To better evaluate the effectiveness of current defense methods against diffusion-based editing methods, in this work, we further construct a dataset, named Defense Edit. We hope this dataset can draw attention to the privacy protection challenges posed by diffusion-based image editing models. ... We contribute a dataset called Defense-Edit for evaluating the defense performance against editing-based methods. ... Specifically, we conduct experiments using the 100 unique identifiers (IDs) gathered from VGGFace2 (Cao et al. 2018) and Celeb A-HQ (Karras et al. 2017) datasets. |
| Dataset Splits | No | The paper mentions using 100 unique identifiers from VGGFace2 and Celeb A-HQ datasets and generating 16 images under 5 different seeds for evaluation. It also states following 'the dataset usage of the Anti-DB' for training Dream Booth/Lo RA models, but it does not specify explicit training/validation/test splits, percentages, or sample counts for the experiments conducted in this paper. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU models, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions) required to replicate the experiments. |
| Experiment Setup | Yes | To ensure a fair comparison, following Anti-DB, we adopt the noise budget of η = 0.05 for all these methods. During the evaluation process, for each trained Dream Booth/Lo RA model, we generate 16 images under 5 different seeds, totaling 80 images, to evaluate the corresponding results, thereby eliminating the variability associated with a single seed. |