Black-Box Adversarial Attack on Time Series Classification
Authors: Daizong Ding, Mi Zhang, Fuli Feng, Yuanmin Huang, Erling Jiang, Min Yang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three real-world TSC datasets and five DNN based models validate the effectiveness of Black Tree S, e.g., it improves the attack success rate from 19.3% to 27.3%, and decreases the detection success rate from 90.9% to 6.8% for LSTM on the UWave dataset. |
| Researcher Affiliation | Academia | Daizong Ding1, Mi Zhang1, Fuli Feng2, Yuanmin Huang1, Erling Jiang1, Min Yang1* 1 School of Computer Science, Fudan University, China 2University of Science and Technology of China {17110240010@, mi zhang@, yuanminhuang21@m., eljiang21@m., m yang@}fudan.edu.cn fulifeng93@gmail.com |
| Pseudocode | Yes | Algorithm 1 in Appendix C summarizes the overall framework. |
| Open Source Code | No | The paper does not explicitly state that source code for the described methodology is publicly available, nor does it provide any links to a code repository. |
| Open Datasets | No | We conduct the experiments on three time series classification datasets: Uwave, Climate and Eye. |
| Dataset Splits | No | Table 4 provides the sizes for 'Train Set' and 'Test Set' for each dataset, but there is no explicit mention of a validation set or specific details about the data splitting methodology (e.g., percentages, random seed, cross-validation). |
| Hardware Specification | Yes | All the experiments are conducted on a machine with a 20-core CPU, 256GBs of memory and 5 NVIDIA RTX 2080Ti GPUs. |
| Software Dependencies | No | The paper mentions optimizers (RMSProp, Adam) and general model types (CNN, RNN, self-attention), but it does not specify software libraries or frameworks with version numbers (e.g., TensorFlow 2.x, PyTorch 1.x) that were used. |
| Experiment Setup | Yes | For all DNN based classifiers, the hidden size and the learning rate are set as 20 and 0.005 respectively. The optimizer of RNN is the RMSProp, while the optimizer of the CNN and self-attention model is the Adam (Diederik, Jimmy et al. 2015). For the Black Tree S, the K is 20 and the maximal size of perturbed positions is 100. We adopt a quadtree to perform the tree search strategy. For ϵ, the default value is set as 0.3, which is widely used in previous adversarial attacks on TSC models (Oregi et al. 2018). |