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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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. |