Copyright (C) 2020 Anonymous Authors of ICLR-2021 Submission #471
USE THE CODE FOR ACADEMIC PURPOSE ONLY.| Step # | Description | Command |
|---|---|---|
| 1 | Download the datasets | python3 Download.py |
| 2.1 | Train network on fasion | python3 Train.py -D cuda -M faC_c2f2 |
| 2.2 | Train network on sop | python3 Train.py -D cuda -M sopE_res18 |
| 3 | Conduct white-box attack experiments | bash bin/wtable |
| 4.1.1 | (black box) Rand on fasion | bash bin/farandsearch 5 50 4 |
| 4.1.2 | (black box) Beta on fasion | bash bin/fabatk5 50 4 |
| 4.1.3 | (black box) PSO on fasion | bash bin/fapso 5 50 4 |
| 4.1.4 | (black box) NES on fasion | bash bin/fanes 5 50 4 |
| 4.1.5 | (black box) SPSA on fasion | bash bin/faspsa 5 50 4 |
| 4.2.1 | (black box) SPSA on SOP | bash bin/randsearch 5 50 4 |
| 4.2.2 | (black box) SPSA on SOP | bash bin/batk 5 50 4 |
| 4.2.3 | (black box) SPSA on SOP | bash bin/pso 5 50 4 |
| 4.2.4 | (black box) SPSA on SOP | bash bin/nes 5 50 4 |
| 4.2.5 | (black box) SPSA on SOP | bash bin/spsa 5 50 4 |
The syntax of the bin/* black-box attack commands is bin/<algorithm> k N varepsilon*255.
.
├── Attack.py | entrance script for white-box order attack
├── bin | collection of shortcut scripts
│ ├── batk | Beta on sop
│ ├── batkparam | Beta parameter search example
│ ├── batktable | Beta batched experiments
│ ├── fabatk | Beta on fashion
│ ├── fanes | NES on fashion
│ ├── fapso | PSO on fashion
│ ├── fapsops | PSO parameter search example
│ ├── farandsearch | Rand on fashion
│ ├── faspsa | SPSA on fashion
│ ├── nes | NES on sop
│ ├── nesparam | NES parameter search example
│ ├── nestable | NES batched experiments
│ ├── nodrtable | Ablation: no dimension reduction
│ ├── pso | PSO on sop
│ ├── psoparam | PSO parameter search example
│ ├── psotable | PSO batched experiments
│ ├── qbudcurve.py | plot a figure in appendix
│ ├── randsearch | Rand on sop
│ ├── randsearchtable | Rand batched experiments
│ ├── spsa | SPSA on sop
│ ├── spsaparam | SPSA parameter search example
│ ├── spsatable | SPSA batched experiments
│ ├── wloss.py | plot a figure in main manuscript
│ ├── wloss-sop.py | plot a figure in appendix
│ └── wtable | white-box batched experiments
├── BlackOA.py | entrance script for black-box order attack
├── config.yml | configuration file for models and attacks
├── display.py | display query results from SnapShop
├── _download.py | helper utility used for download files
├── Download.py | download the fashion minst dataset
├── lib | core algorithms
│ ├── common.py | white-box order attack implementation
│ ├── datasets | dataset abstractions
│ │ ├── fashion.py | fashion-mnist dataset
│ │ ├── __init__.py | python file
│ │ └── sop.py | stanford-online-products
│ ├── faC_c2f2.py | fashion c2f2 network with cosine metric
│ ├── faC_lenet.py | fashion lenet with cosine metric
│ ├── faC_res18.py | fashion resnet18
│ ├── faE_c2f2.py | fashion c2f2 network with euclidean metric
│ ├── __init__.py | python file
│ ├── rankingmodel.py | abstract class
│ ├── reorder.py | black-box order attack implementation
│ ├── snapshop.py | snapshop client and abstraction
│ ├── sopE_res18.py | sop resnet18 with euclidean metric
│ ├── sopE_res50.py | sop resnet50 with euclidean metric
│ └── utils.py | miscellaneous
├── poc | miscellaneous
│ └── taumap.py | miscellaneous
├── PracticalOA.py | entrance script for practical order attack
├── Train.py | entrance script for training ranking model
└── visrow.py | visualization helper for snapshop attackPython 3.8.3
PyTorch 1.6.0
Numpy 1.18.5
Scipy 1.5.0
CUDA 10.2
Linux 5.7