Classifier-cOnfidence gUided Purification (COUP)

Implementation code for the paper "Classifier Guidance Enhances Diffusion-based Adversarial Purification by Preserving Predictive Information", accepted by ECAI 2024.

Abstract

Adversarial purification is one of the promising approaches to defend neural networks against adversarial attacks. Recently, methods utilizing diffusion probabilistic models have achieved great success for adversarial purification in image classification tasks. However, such methods fall into the dilemma of balancing the needs for noise removal and information preservation. This paper points out that existing adversarial purification methods based on diffusion models gradually lose sample information during the core denoising process, causing occasional label shift in subsequent classification tasks. As a remedy, we suggest to suppress such information loss by introducing guidance from the classifier confidence. Specifically, we propose Classifier-cOnfidence gUided Purification (COUP) algorithm, which purifies adversarial examples while keeping away from the classifier decision boundary. Experimental results show that COUP can achieve better adversarial robustness under strong attack methods.

Requirements

We follow the requirements of DiffPure: https://github.com/NVlabs/DiffPure/tree/master

• Python 3.8

• CUDA=11.0

• Installation of the required library dependencies with Docker:

  docker build -f diffpure.Dockerfile --tag=diffpure:0.0.1 .
  docker run -it -d --gpus 0 --name diffpure --shm-size 8G -v $(pwd):/workspace -p 5001:6006 diffpure:0.0.1
  docker exec -it diffpure bash

Dataset

We use CIFAR-10 dataset which can be automatically download in the code.

Checkpoint

You have to download the checkpoint and put it in the 'pretrained' directory.

• Diffusion model
  • We use the VP-SDE (vp/cifar10_ddpmpp_deep_continuous) of Score SDE.
• Classifier
  • We use both WideResNet-28-10(no need to download separately) and WideResNet-70-16

To get the results of AutoAttack of our COUP:

• Linf

  cd run_scripts/cifar10
  bash run_cifar_stand_inf_guide.sh 121 0 # standard mode
  bash run_cifar_rand_inf_guide.sh 121 0 # rand mode

• L2

  cd run_scripts/cifar10
  bash run_cifar_stand_L2_guide.sh 121 0 # standard mode
  bash run_cifar_rand_L2_guide.sh 121 0 # rand mode