Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
CoSDA: Enhancing the Robustness of Inversion-based Generative Image Watermarking Framework
Authors: Han Fang, Kejiang Chen, Zijin Yang, Bosen Cui, Weiming Zhang, Ee-Chien Chang
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that Co SDA effectively enhances watermark robustness while maintaining the visual quality of generated images. [...] To assess the robustness of Co SDA, we adopt the evaluation settings from (Yang et al. 2024), encompassing both the detection and traceability scenarios. [...] All results are obtained from the testing on 50 watermarked images generated with randomly sampled prompts from Stable-Diffusion Prompt. [...] Abaltion Study |
| Researcher Affiliation | Academia | 1 National University of Singapore 2 University of Science and Technology of China |
| Pseudocode | No | The paper describes its methodology and processes using mathematical equations and textual explanations, but it does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper discusses the use of existing frameworks and models like Stable Diffusion from Hugging Face, Gaussian Shading, and Tree-Rings. However, it does not provide any explicit statement or link for the open-sourcing of the Co SDA methodology developed in this paper. |
| Open Datasets | Yes | During inference, we employ prompts from the Stable-Diffusion Prompt(Gustavosta 2022). https://huggingface.co/datasets/Gustavosta/Stable Diffusion-Prompts. Accessed: 2025-01-10. |
| Dataset Splits | No | The paper states that 'All results are obtained from the testing on 50 watermarked images generated with randomly sampled prompts from Stable-Diffusion Prompt.' It also mentions training a drift alignment network ΘDA with 'pairs of distorted-benign latent representations.' However, specific training, validation, or test dataset splits (e.g., percentages, exact counts, or predefined splits with citations) for the data used to train ΘDA are not provided. |
| Hardware Specification | Yes | All experiments are performed using Py Torch 1.12.1 and a single NVIDIA-A40 GPU. |
| Software Dependencies | Yes | All experiments are performed using Py Torch 1.12.1 and a single NVIDIA-A40 GPU. |
| Experiment Setup | Yes | The sampling step and guidance scale are set to 50 and 7.5, respectively. [...] For training and testing with ΘDA, we conduct DDIM inversion with condition and 10 steps. [...] Considering both visual quality and inversion error, we set p = 0.8 for subsequent comparison experiments. [...] The network ΘDA consists of one Single-Conv (the concatenation of Conv-BN-Re LU ), three Res-Block s (He et al. 2016) and one Conv block. To train ΘDA, we minimize the difference between ˆzm V and zm T . The loss function L is set as: L = ˆzm V zm T 2 2 |