First-order adversarial vulnerability of neural networks and input dimension: Code and Supplements
This repository contains code to accompany the ICML 2019 paper First-order adversarial vulnerability of neural networks and input dimension.
Special packages used:
- pytorch (tested on 1.0.0)
- foolbox (tested on 2.0.0)
- Train a net with default configuration and compute its vulnerability:
- Train a net on CIFAR10 with PGD and compute its vulnerability, with simililar settings than Section 4.1:
python main.py --config configs/config_cifar_pgd.txt
- Train a net on upsampled CIFAR10 (bCIFAR10) and compute its vulnerability, with similar settings than Section 4.2:
python main.py --config configs/config_bcifar.txt
- Any parameter from the config file can be overwritten from the command line, as in:
python main.py --config configs/config_bcifar.txt --img_size 64 --log_step 10
See files in configs/ for other typical experiment settings/arguments.
The experiments in Section 4.2 actually do not use the vulnerabilities contained
in the vulnerability_XX.pt files (computed using the
function from vulnerability.py). Instead, they use the test-set
vulnerabilities contained in state['logs'] of the file last.pt (and computed
using our custom pgd implementation from penalties.py), averaged over the 20
last training epochs.
The code was implemented for 3 datasets:
- CIFAR10 (and upsampled CIFAR10, see paper Sec 4.2)
- a custom 12-class mini-ImageNet dataset (see below & paper Sec 4.2).
Getting our custom 12-class Mini-ImageNet Dataset
The custom mini-ImageNet was constructed by merging some similar classes together from the usual ImageNet dataset (for example all the dogs). If the resulting class got too big (e.g. the dog-class), we sampled a random subset of it. The 12 classes obtained were:
ball - car - cat - dog - elephant - fish - monkey - plane - ship - string-instrument - truck - wind-instrument
The list of images used in each class can be found in the imgnet12/ folder. To use our mini-imagenet dataloader, create a folder named imgnet12/train/ (resp. imgnet12/val/), with 12 subfolders (1 per class; name irrelevant) containing the images listed in train.txt (resp. val.txt).
This source code is released under a Attribution-NonCommercial 4.0 International license, find out more about it here.