Disguise without Disruption: Utility-Preserving Face De-identification
Authors: Zikui Cai, Zhongpai Gao, Benjamin Planche, Meng Zheng, Terrence Chen, M. Salman Asif, Ziyan Wu
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We extensively evaluate our method using multiple datasets, demonstrating a higher deidentification rate and superior consistency compared to prior approaches in various downstream tasks. |
| Researcher Affiliation | Collaboration | 1University of California, Riverside, CA 2United Imaging Intelligence, Burlington, MA 3Rensselaer Polytechnic Institute, Troy, NY |
| Pseudocode | No | The paper describes the proposed architecture and process using figures and equations, but it does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We train our models on VGGFace2 dataset (Cao et al. 2018)... Evaluation datasets include LFW (Huang et al. 2008)... Celeb A-HQ (Karras et al. 2017)... and WFLW (Wu et al. 2018). |
| Dataset Splits | Yes | Taking facial landmark detection as an example on the WFLW dataset (Wu et al. 2018) (98 landmarks per image), we split data into training/testing sets (7,500/2,500) |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions models and optimizers (e.g., HRNetv2-W18 model, Adam optimizer) but does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | We use an HRNetv2-W18 model... trained for 60 epochs with Adam optimizer (Kingma and Ba 2014) (β1 = 0, β2 = 0.999), learning rate 10 4, and batch size 64. |