Mesh Watermark Removal Attack and Mitigation: A Novel Perspective of Function Space
Authors: Xingyu Zhu, Guanhui Ye, Chengdong Dong, Xiapu Luo, Shiyao Zhang, Xuetao Wei
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that FUN CEVA D E achieves 100% evasion rate among all previous watermarking methods while achieving only 0.3% evasion rate on FUN CMAR K. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, Southern University of Science and Technology, China 2Department of Computing, Hong Kong Polytechnic University, Hong Kong EMAIL, EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes methods through mathematical formulations and descriptive text, such as equations (1) to (7) and detailed explanations of FUNCEVADE and FUNCMARK, but does not include a distinct pseudocode block or algorithm section. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for the described methodology, nor does it include a link to a code repository. It mentions using a third-party package 'mesh-to-sdf' with its link, but this is not the authors' own implementation code. |
| Open Datasets | Yes | We normalize meshes in Shape Net (Chang et al. 2015) and Stanford Repo (Laboratory 2023) to [ 1, 1]3. |
| Dataset Splits | No | The paper mentions using ShapeNet and Stanford Repo datasets but does not explicitly provide training, validation, or test dataset splits, or reference any predefined splits with specific details like percentages, sample counts, or citations for such splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run experiments, such as GPU or CPU models, processor types, or memory specifications. |
| Software Dependencies | No | The paper mentions using 'mesh-to-sdf', 'SIREN', and 'marching cube' as software components, but it does not specify any version numbers for these or any other software dependencies, which would be necessary for reproducible setup. |
| Experiment Setup | Yes | We use Adam optimizer with the initialized learning rate 10-3 for 1000 epochs, and we decrease the learning rate by half every 200 epochs. We set Ns = 32 (i.e., the spherical system is divided into 32 32 partitions). We set message length n = 48, and the detection threshold τ = 31. ... and the default watermarking strength δ = 0.001. |