Defense Against Backdoor Attacks in SSL

Introduction

This repository contains code for our submission to ICLR workshop 24. We implement backdoor-poisoning attacks and evaluate our defense against these attacks on Image Classification Task. Attacks implemented:

• CTRL: ICCV 2023
• FIBA: CVPR 2022
• HTBA: CVPR 2022

Defenses implemented:

• Gaussian Blur (Invariance-Equivariance)
• Luminance (Exploiting evasiveness)

Datasets implemented:

• CIFAR 10
• CIFAR 100
• IMAGENET 100 (100 class subset of IMAGENET)

We show a successful and partly generalizable defense against backdoor attacks in SSL and lay the theoretical foundation for defense against backdoor attacks in Semantic Segmentation and Object Detection tasks.

Installation and Requirements

• Download the repository from anonymous4openscience.
• Make a virtual environment (optional) (recommended)

  virtualenv <env_name>
  source <env_name>/bin/activate

• Install necessary libraries

  pip install -r requirements.txt

Running The Code

We provide a bash script to run our program with appropriate command-line arguments.

• Give permission

chmod +x run.sh

• Call main_train.py

bash run.sh <--args values>

Call bash run.sh --help if unsure about the arguments, available options or their meaning.

Results

Results of all experiments are saved in a folder named saves. Each experiment will create a folder named <job name> set by run.sh. Each experiment folder contains model state-dicts and optimizer states saved every 100 epoch, and a tfenvent file containing tensorboard log. To view training progress and compare training curves:

tensorboard --logdir=saves

When repeating the same experiment with different hyperparameters, use --suffix option in run.sh to prevent overwriting log of previous experiment.

To Do

• Implement ImageNet 100
• Implement MoCo v2
• Remove unnecessary command line arguments and refactor code.

NOTE: This repository is a fork of CTRL's repository with refactoring, implementation of defenses and other attacks.