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BAFFLE: Hiding Backdoors in Offline Reinforcement Learning Datasets

Intro

Replication Package for "BAFFLE: Hiding Backdoors in Offline Reinforcement Learning Datasets", IEEE S&P (Oakland) 2024.

Overview

Reinforcement learning (RL) makes an agent learn from trial-and-error experiences gathered during the interaction with the environment. Recently, offline RL has become a popular RL paradigm because it saves the interactions with environments. In offline RL, data providers share large pre-collected datasets, and others can train high-quality agents without interacting with the environments. This paradigm has demonstrated effectiveness in critical tasks like robot control, autonomous driving, etc. However, less attention is paid to investigating the security threats to the offline RL system. This paper focuses on backdoor attacks, where some perturbations are added to the data (observations) such that given normal observations, the agent takes high-rewards actions, and low-reward actions on observations injected with triggers. In this paper, we propose Baffle Backdoor Attack for Offline Reinforcement Learning), an approach that automatically implants backdoors to RL agents by poisoning the offline RL dataset, and evaluate how different offline RL algorithms react to this attack. Our experiments conducted on four tasks and four offline RL algorithms expose a disquieting fact: none of the existing offline RL algorithms is immune to such a backdoor attack. More specifically, Baffle modifies 10% of the datasets for four tasks (3 robotic controls and 1 autonomous driving). Agents trained on the poisoned datasets perform well in normal settings. However, when triggers are presented, the agents' performance decreases drastically by 63.2%, 53.9%, 64.7%, and 47.4% in the four tasks on average. The backdoor still persists after fine-tuning poisoned agents on clean datasets. We further show that the inserted backdoor is also hard to be detected by a popular defensive method. This paper calls attention to developing more effective protection for the open-source offline RL dataset.

Models

Please check our agents' parameters in this anonymous link:

Please check our the poisoned dataset in this link:

The descriptions of folds are as follows: (folders?)

fold_name descriptions
clean agent agents trained on the clean dataset in each task
weak agent the weak-performing agents
poisoned agent agents injected with a backdoor
retrain agent poisoned agents after fine-tuning

Selected offline RL algorithms

algorithm discrete control continuous control
Advantage Weighted Actor-Critic (AWAC) x ✓
Behavior Cloning (supervised learning) ✓ ✓
Batch Constrained Q-learning (BCQ) ✓ ✓
Bootstrapping Error Accumulation Reduction (BEAR) x ✓
Conservative Q-Learning (CQL) ✓ ✓
Implicit Q-learning (IQL) x ✓
Policy in the Latent Action Space with Perturbation (PLAS-P) x ✓
Offline Soft Actor-Criti (SAC-off) ✓ ✓
Twin Delayed Deep Deterministic Policy Gradient plus Behavioral Cloning (TD3PlusBC) x ✓

Result presentations

The videos of the agent's behaviors under the normal scenario and the triggered scenario are in the folder videos.

Project structure

The structure of this project is as follows：

MuJoCo
    -- mujoco_bear.py ------------------ train the clean agents using the BEAR algorithm.
    -- mujoco_bc.py ------------------ train the clean agents using the BC algorithm.
    -- mujoco_bcq.py ------------------ train the clean agents using the BCQ algorithm.
    -- mujoco_bear.py ------------------ train the clean agents using the BEAR algorithm.
    -- mujoco_cql.py ------------------ train the clean agents using the CQL algorithm.
    -- mujoco_iql.py ------------------ train the clean agents using the IQL algorithm.
    -- mujoco_plasp.py ------------------ train the clean agents using the PLASP algorithm.
    -- mujoco_sac.py ------------------ train the clean agents using the SAC algorithm.
    -- mujoco_td3plusbc.py ------------------ train the clean agents using the TD3PLUSBC algorithm.
    -- poisoned_mujoco_xx.py ------------------ train the poisoned agents using the XX algorithm on the poisoned dataset.
    -- retrain_mujoco_xx.py ------------------ retrain the poisoned agents using the XX algorithm.
    -- mujoco_poisoned_dataset.py ------------------ generate the misleading experiences.
    -- perturbation_influence.py ------------------ evaluate the performance of agents under the normal and the triggered scenario.
    -- plot.py ------------------ visualize the performance of agents.
    -- env_info.py ------------------ out the information of the selected tasks.
    -- backdoor_detection.py ------------------ detecting poisoning dataset using activation clustering.
    -- vedio_record.py ------------------ recording the vedio of envrionments.
    params ------------------ the files of hype-parameters settings of offline reinforcement learning algorithms.
    
CARLA
    -- cql-carla-lane-v0.py ------------------ train the clean agents using our selected offline RL algorithms.
    -- poisoned_dataset.py ------------------ generate the misleading experiences.
    -- poisoned-cql-carla-lane-v0.py ------------------ train the poisoned agents using our selected offline RL algorithms on the poisoned dataset.
    -- retrain_carla.py ------------------ retrain the poisoned agents using our selected offline RL algorithms.
    -- carla_perturbation.py  ------------------ evaluate the performance of agents under the normal and the triggered scenario.
    -- backdoor_detection.py ------------------ detecting poisoning dataset using activation clustering. 
    
Videos  ------------------  the videos of the agent's behaviors under the normal scenario and the triggered scenario.

Installation

This code was developed with python 3.7.11.

The version of Mujoco is Mujoco 2.1.0.

1. Install d3rlpy and mujoco-py:

The installation of mujoco can be found here:

pip install -e . (install d3rlpy)
pip install mujoco-py==2.1.2.14
pip install gym==0.22.0
pip install scikit-learn==1.0.2
pip install Cython==0.29.36

2. Setup Carla:

Download and unzip Carla:

wget https://carla-releases.s3.eu-west-3.amazonaws.com/Linux/CARLA_0.9.8.tar.gz
wget https://carla-releases.s3.eu-west-3.amazonaws.com/Linux/AdditionalMaps_0.9.8.tar.gz

Add the following environment variables in your bashrc or zshrc:

export PYTHONPATH=$PYTHONPATH:/home/CARLA_0.9.8/PythonAPI
export PYTHONPATH=$PYTHONPATH:/home/CARLA_0.9.8/PythonAPI/carla
export PYTHONPATH=$PYTHONPATH:/home/CARLA_0.9.8/PythonAPI/carla/dist/carla-0.9.8-py3.5-linux-x86_64.egg

Install the following extra libraries:

pip install pygame
pip install networkx
pip install dotmap

3. Install dm-control and mjrl:

pip install dm_control==0.0.425341097
git clone https://github.com/aravindr93/mjrl.git
cd mjrl 
pip install -e .

4. Install d4rl:

pip install patchelf
git clone https://github.com/rail-berkeley/d4rl.git
cd d4rl
git checkout 71a9549f2091accff93eeff68f1f3ab2c0e0a288

Replace setup.py with:

  from distutils.core import setup
  from platform import platform
  from setuptools import find_packages

  setup(
     name='d4rl',
     version='1.1',
     install_requires=['gym',
                       'numpy',
                       'mujoco_py',
                       'pybullet',
                       'h5py',
                       'termcolor', 
                       'click'], 
     packages=find_packages(),
     package_data={'d4rl': ['locomotion/assets/*',
                            'hand_manipulation_suite/assets/*',
                            'hand_manipulation_suite/Adroit/*',
                            'hand_manipulation_suite/Adroit/gallery/*',
                            'hand_manipulation_suite/Adroit/resources/*',
                            'hand_manipulation_suite/Adroit/resources/meshes/*',
                            'hand_manipulation_suite/Adroit/resources/textures/*',
                            ]},
     include_package_data=True,
 )

Then:

pip install -e .

Please refer to rl_poisoner.yaml for more detailed configuration information.

How to run

⚠️ The scripts for replicating our experiments can be found in README.md under the folders: carla and mujoco.

Contact

Acknowledgement

  • The codes for achieving the offline RL algorithms are based on the D3RLPY.
  • The offline datasets for our evaluations are from D4RL.