Distilling Cognitive Backdoor Patterns within an Image
Authors: Hanxun Huang, Xingjun Ma, Sarah Monazam Erfani, James Bailey
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to show that CD can robustly detect a wide range of advanced backdoor attacks. |
| Researcher Affiliation | Academia | 1School of Computing and Information Systems, The University of Melbourne, VIC, Australia 2School of Computer Science, Fudan University, Shanghai, China |
| Pseudocode | Yes | Algorithm 1 Unlearning and Fine-tuning |
| Open Source Code | Yes | Code is available at https://github.com/Hanxun H/Cognitive Distillation. |
| Open Datasets | Yes | We perform evaluations on 3 datasets, including CIFAR-10 (Krizhevsky et al., 2009), an Image Net (Deng et al., 2009) subset (200 classes), and GTSRB (Houben et al., 2013). Also, Celeb A (Liu et al., 2015) is used for bias detection. |
| Dataset Splits | No | The paper evaluates performance on training and test sets, but does not explicitly detail a separate validation split for model training. |
| Hardware Specification | Yes | All experiments are run with NVIDIA Tesla P100/V100/A100 GPUs with Py Torch implementations. |
| Software Dependencies | No | The paper mentions 'Py Torch implementations' but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | For CIFAR10 experiments, we train for 60 epochs with SGD optimizer, weight decay 5 10 4, initial learning rate 0.1 decay by 0.1 at the 45th epoch. For our CD method, we use Adam optimizer (Kingma & Ba, 2014) with initial learning rate 0.1, β1=0.1, β2=0.1, and a total of 100 steps to learn the input mask. We set pb to 2.5% and pc to 70%, and optimize for 5 epochs with learning rate set to 5 10 4. |