Preventing Latent Diffusion Model-Based Image Mimicry via Angle Shifting and Ensemble Learning
Authors: Minghao Li, Rui Wang, Ming Sun, Lihua Jing
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that the alternating iterative framework and the stable optimization strategy on cosine similarity loss are more efficient and more effective. 5 Experiments 5.1 Setup Datasets We evaluate our methods on two datasets. |
| Researcher Affiliation | Academia | Institute of Information Engineering, Chinese Academy of Sciences School of Cyber Security, University of Chinese Academy of Sciences EMAIL |
| Pseudocode | No | The paper describes methods and a pipeline diagram (Figure 4), but does not contain a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Code is available at https://github.com/Minghao Li01/cosattack. |
| Open Datasets | Yes | We evaluate our methods on two datasets. Considering that infringement issues mainly occur on human faces and artworks, we use a subset of the Celeb A-HQ [Karras et al., 2018] and a subset of Wiki Art [Nichol, 2016] respectively. |
| Dataset Splits | No | We randomly select 500 face images from Celeb A-HQ. The Wiki Art dataset contains artworks from 27 different styles. We randomly selected 20 images from each style of artworks. The text describes how images were *selected* for evaluation but does not specify standard training/validation/test splits of a dataset for model training. |
| Hardware Specification | Yes | we conduct experiments on NVIDIA Ge Force GTX 1080Ti with 12G VRAM. |
| Software Dependencies | No | The paper mentions tools like SDEdit, SD-v1-4, and DDIM100 but does not provide specific version numbers for the software dependencies used in their implementation (e.g., Python, PyTorch versions). |
| Experiment Setup | Yes | Following the existing research, we use the ℓ -norm to constrain the generated adversarial examples, with the constraint range as 8/255 and the step size α = 1/255. To facilitate the exploration of the impact of the grouping strategy, we set the number of iterations K = 100 for all the methods. For grouping strategy, we set N M = 20 5. |