UAN
Code for Learning Universal Adversarial Perturbations with Generative Models
In this paper, we use generative models to compute universal adversarial perturbations. The generator is not conditioned on the images and so creates a perturbation that can be applied to any image to create an adversarial example.
We get pretty pictures like this:
Clean Image + Perturbation == Adversarial Image
Here is the output of a UAN throughout training:
Data set-up
For ImageNet
- Follow instructions https://github.com/amd/OpenCL-caffe/wiki/Instructions-to-create-ImageNet-2012-data . The validation set should be in path
./imagenet/data/val/. There should be 1000 directories, each with 50 images.
For CIFAR-10
- Attack code will download if dataset does not exist.
Target model training steps:
For ImageNet
- Clone https://github.com/Cadene/pretrained-models.pytorch into this directoy and (for easy python importing) rename to "pretrained_models_pytorch"
For CIFAR-10
- Train some models using https://github.com/kuangliu/pytorch-cifar
To run the attack, choose between ImageNet and CIFAR-10 and specify the model.
e.g. python main.py --cuda --dataset ImageNet --epochs 200 --batchSize 32 --shrink 0.00075 --shrink_inc 0.0001 --l2reg 0.00001 --restrict_to_correct_preds 1 --netClassifier resnet152 --imageSize 224 --outf resnet-results --every 100
Note: For best results on ImageNet, batch size needs to be large. This takes up a lot of memory.