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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Data-Efficient Backdoor Attacks
Authors: Pengfei Xia, Ziqiang Li, Wei Zhang, Bin Li
IJCAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 16,345 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,537 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on CIFAR-10 and Image Net-10 indicate that the proposed method is effective: the same attack success rate can be achieved with only 47% to 75% of the poisoned sample volume compared to the random selection strategy. |
| Researcher Affiliation | Academia | University of Science and Technology of China, Hefei, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Filtering-and-Updating Strategy |
| Open Source Code | Yes | The prototype code of our method is now available at https://github. com/xpf/Data-Efficient-Backdoor-Attacks. |
| Open Datasets | Yes | We perform experiments on CIFAR-10 [Krizhevsky and Hinton, 2009] and Image Net-10 to test the effectiveness of the proposed method. |
| Dataset Splits | No | The paper refers to training and test sets but does not explicitly specify a validation dataset split (e.g., percentages or counts for a validation set). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU models, CPU types). |
| Software Dependencies | No | The paper mentions optimizers (SGD and ADAM) but does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | The total training duration is 60, and the batch size is 512 for CIFAR-10 and 256 for Image Net-10. The initial learning rate is set to 0.01 and is dropped by 10 after 30 and 50 epochs. ... α is set to 0.5 and N is set to 10, if not otherwise specified. |