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..
Mind Control through Causal Inference: Predicting Clean Images from Poisoned Data
Authors: Mengxuan Hu, Zihan Guan, Yi Zeng, Junfeng Guo, Zhongliang Zhou, Jielu Zhang, Ruoxi Jia, Anil Vullikanti, Sheng Li
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our model can effectively and robustly recover the original true labels of backdoored images, without compromising clean accuracy. Our code can be found at https://github.com/xuanxuan03021/BKD BKD ICLR. Table 1 compares our method MCCI with other defense baselines across various backdoor attacks on two datasets. |
| Researcher Affiliation | Collaboration | 1University of Virginia 2Virginia Tech 3University of Maryland, College Park 4Merck & Co., Inc. 5University of Georgia |
| Pseudocode | Yes | Algorithm 1: Mind Control Through Causal Inference (MCCI) |
| Open Source Code | Yes | Our code can be found at https://github.com/xuanxuan03021/BKD BKD ICLR. |
| Open Datasets | Yes | Following (Guo et al., 2023a; Gao et al., 2019; Li et al., 2021a), we choose two widely-adopted datasets for evaluating the effectiveness of our proposed method: CIFAR10 (Krizhevsky, 2009), and Image Net-subset (Deng et al., 2009). |
| Dataset Splits | Yes | The details of the dataset are given in Table 5. Table 5: Statistical information about the Datasets Dataset Image Size # of Training samples # of Testing Samples # of Classes CIFAR-10 32 32 3 50,000 10,000 10 Image Net-Subset 224 224 3 9,469 3,925 10 |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions specific tools and models like 'open-sourced backdoor learning toolbox (Li et al., 2023)', 'Vi T', 'CLIP', 'BLIP', 'Res Net-18', 'Efficient Net', but does not provide specific version numbers for any software dependencies (e.g., Python, PyTorch, TensorFlow, CUDA). |
| Experiment Setup | Yes | We use an initial learning rate of 0.1 that is decreased by a factor of 10 at epochs 30, 60, and 90, 100 epochs, a batch size of 128, and a weight decay of 1e-4 for training the defense model against all attack baselines. |