Learning Transferable Adversarial Examples via Ghost Networks
Authors: Yingwei Li, Song Bai, Yuyin Zhou, Cihang Xie, Zhishuai Zhang, Alan Yuille11458-11465
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we give a comprehensive experimental evaluation of the proposed Ghost Networks. |
| Researcher Affiliation | Academia | 1Johns Hopkins University 2University of Oxford |
| Pseudocode | No | The paper provides mathematical formulations and figures but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/Li Yingwei/ ghost-network. ... We release source code and provide additional experimental results in https://github.com/Li Yingwei/ghost-network. |
| Open Datasets | Yes | We select 5000 images from the ILSVRC 2012 validation set... Imagenet: A large-scale hierarchical image database (Deng et al. 2009). The Neur IPS 2017 Adversarial Challenge also uses Image Net (Deng et al. 2009). |
| Dataset Splits | No | The paper uses pre-trained base models and selects 5000 images from the ILSVRC 2012 validation set for testing. It does not provide explicit training/validation/test splits for the models they develop or attack, as their method does not involve training new models from scratch. |
| Hardware Specification | No | The paper does not specify the exact hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names with specific versions like PyTorch 1.x or Python 3.x). |
| Experiment Setup | Yes | If not specified otherwise, we follow the default settings in Kurakin, Goodfellow, and Bengio (2017a), i.e., step size α = 1 and the total iteration number N = min(ϵ + 4, 1.25ϵ). We set the maximum perturbation ϵ = 8 (the iteration number N = 10 in this case). For the momentum term, the decay factor μ is set to be 1 as in Dong et al. (2018). |