Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Towards Stable Backdoor Purification through Feature Shift Tuning
Authors: Rui Min, Zeyu Qin, Li Shen, Minhao Cheng
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 35,378 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,593 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 | Extensive experiments demonstrate that our FST provides consistently stable performance under different attack settings. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science & Engineering, HKUST 2JD Explore Academy |
| Pseudocode | Yes | Algorithm 1 Feature Shift Tuning (FST) |
| Open Source Code | Yes | Our codes are available at https: //github.com/AISafety-HKUST/stable_backdoor_purification. |
| Open Datasets | Yes | We conduct experiments on four widely used image classification datasets, CIFAR-10 [15], GTSRB [32], Tiny-Image Net [8], and CIFAR-100 [15]. |
| Dataset Splits | Yes | Following the previous work [41] and leave 2% of original training data as the tuning dataset. For the CIFAR100 and Tiny-Image Net, we note that a small tuning dataset would hurt the model performance and therefore we increase the tuning dataset to 5% of the training set. |
| Hardware Specification | Yes | We conducted all the experiments with 4 NVIDIA 3090 GPUs. |
| Software Dependencies | No | The paper mentions 'Py Torch' as a provider of pre-trained weights but does not specify version numbers for PyTorch or any other software dependencies crucial for replication. |
| Experiment Setup | Yes | For our FST, we adopt SGD with an initial learning rate of 0.01 and set the momentum as 0.9 for both CIFAR-10 and GTSRB datasets and decrease the learning rate to 0.001 for both CIFAR-100 and Tiny-Image Net datasets to prevent the large degradation of the original performance. We fine-tune the models for 10 epochs on the CIFAR-10; 15 epochs on the GTSRB, CIFAR-100 and Tiny-Image Net. We set the α as 0.2 for CIFAR-10; 0.1 for GTSRB; 0.001 for both the CIFAR-100 and Tiny-Image Net. |