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..
SampDetox: Black-box Backdoor Defense via Perturbation-based Sample Detoxification
Authors: Yanxin Yang, Chentao Jia, DengKe Yan, Ming Hu, Tianlin Li, Xiaofei Xie, Xian Wei, Mingsong Chen
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments demonstrate the effectiveness of Samp Detox in defending against various state-of-the-art backdoor attacks. |
| Researcher Affiliation | Academia | Yanxin Yang1, Chentao Jia1, Deng Ke Yan1, Ming Hu2 , Tianlin Li3, Xiaofei Xie2, Xian Wei1, Mingsong Chen1 1Mo E Eng. Research Center of SW/HW Co-Design Tech. and App., East China Normal University 2Singapore Management University, 3Nanyang Technological University |
| Pseudocode | Yes | Algorithm 1 details the implementation of Samp Detox. |
| Open Source Code | Yes | The source code of this work is publicly available at https://github.com/easywood0204/Samp Detox. |
| Open Datasets | Yes | We investigated three classical datasets (i.e., CIFAR-10 [42], GTSRB [43], and Tiny-Image Net [44]) |
| Dataset Splits | No | For the MS-Celeb-1M dataset, we split the training and tests with a ratio of 8 : 2. Other datasets implicitly use standard splits but no explicit percentages for train/validation/test are universally provided. |
| Hardware Specification | Yes | All experiments were carried out on an Ubuntu workstation equipped with one Intel i7-13700K CPU, 64GB memory, and one NVIDIA Ge Force RTX4090 GPU. |
| Software Dependencies | Yes | we implemented our approach, i.e., Samp Detox, on top of Pytorch (version 1.13.0). |
| Experiment Setup | Yes | The number of total diffusion steps was set to 1000 for all the datasets in the experiments, the noise schedule was set as cosine , and the learning rate was set to 1e-4. Therefore, we suggest to set t1 = 20 and t2 = 120 in practice. |