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Label-Consistent Backdoor Attacks code

This repository contains the code to replicate experiments in our paper:

Label-Consistent Backdoor Attacks Alexander Turner, Dimitris Tsipras, Aleksander Madry https://arxiv.org/abs/1912.02771

The datasets we provide are modified versions of the CIFAR-10 dataset.

Running the code

Step 1: Setup, before doing anything else

Run ./setup.py.

This will download CIFAR-10 into the clean_dataset/ directory in the form of .npy files. It will also download modified forms of the CIFAR-10 training image corpus into the fully_poisoned_training_datasets/ directory, formatted and ordered identically to clean_dataset/train_images.npy. In each corpus, every image has been replaced with a harder-to-classify version of itself (with no trigger applied).

The gan_0_x.npy files use our GAN-based (i.e. latent space) interpolation method with τ = 0.x. The two_x.npy and inf_x.npy files use our adversarial perturbation method with an l₂-norm bound and l_∞-norm bound, respectively, of x.

Finally, this script will install numpy and tensorflow.

Step 2: Generating a poisoned dataset

To generate a poisoned dataset, first edit the last section in config.json. The settings are:

• poisoning_target_class: which (numerical) class is the target class.
• poisoning_proportion: what proportion of the target class to poison.
• poisoning_trigger: which backdoor trigger to use ("bottom-right" or "all-corners").
• poisoning_reduced_amplitude: the amplitude of the backdoor trigger on a 0-to-1 scale (e.g. 0.12549019607843137 for 32/255), or null for maximum amplitude (i.e. 1).
• poisoning_base_train_images: the source of the harder-to-classify images to use for poisoning.

Then, run python generate_poisoned_dataset.py, which will generate the following files in the poisoning_output_dir you specified:

• train_{images,labels}.npy: the poisoned training set (i.e. a proportion of the target class will now be replaced with harder-to-classify images and have the selected trigger applied).
• test_{images,labels}.npy: the CIFAR-10 testing set with the trigger applied to all test images.
• poisoned_train_indices.npy: the indices of all poisoned training images.
• train_no_trigger_images.npy: train_images.npy but without triggers applied.

Step 3: Training a network on the poisoned dataset.

To train a neural network on the poisoned dataset you generated, now edit the other sections in config.json as you wish. The settings include:

• augment_dataset: whether to use data augmentation. If true, uses the function specified by augment_standardization, augment_flip and augment_padding.
• target_class: which (numerical) class is the target class (only used for evaluating the attack success rate).

Then, run python train.py.