Data-Efficient Backdoor Attacks
Authors: Pengfei Xia, Ziqiang Li, Wei Zhang, Bin Li
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 {xpengfei,iceli,zw1996}@mail.ustc.edu.cn, binli@ustc.edu.cn |
| 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. |