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..
UMD: Unsupervised Model Detection for X2X Backdoor Attacks
Authors: Zhen Xiang, Zidi Xiong, Bo Li
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct comprehensive evaluations on CIFAR-10, GTSRB, and Imagenette dataset, and show that our unsupervised UMD outperforms SOTA detectors (even with supervision) by 17%, 4%, and 8%, respectively, in terms of the detection accuracy against diverse X2X attacks. |
| Researcher Affiliation | Academia | Zhen Xiang 1 Zidi Xiong 1 Bo Li 1 1University of Illinois at Urbana-Champaign. |
| Pseudocode | Yes | Algorithm 1 UMD against X2X backdoor attacks |
| Open Source Code | Yes | The code related to this work can be found at: https://github.com/polaris-73/MT-Detection |
| Open Datasets | Yes | Dataset: We consider three benchmark image datasets, CIFAR-10 (Krizhevsky, 2012), GTSRB (Stallkamp et al., 2012), and Imagenette (Deng et al., 2009) |
| Dataset Splits | Yes | In our experiments, we follow the standard train-test split for each dataset (see Apdx. C.1 for details). |
| Hardware Specification | Yes | Empirically, each model inference on CIFAR-10, GTSRB, and Imagenette requires around 0.3h, 2.5h, and 4.3h, respectively, as measured on a single RTX 2080 Ti card. |
| Software Dependencies | No | The paper mentions 'Res Net-18' for model architecture and 'Adam optimizer' for training, but does not provide specific version numbers for software libraries or dependencies like Python or PyTorch. |
| Experiment Setup | Yes | Detailed training configurations are shown in Apdx C.3. |