Overview

BackdoorBench is a comprehensive benchmark of backdoor learning, which studies the adversarial vulnerablity of deep learning models in the training stage. It aims to provide easy implementations of mainstream backdoor attack and defense methods. Currently, we support:

Methods
- 8 Backdoor attack methods: BadNets, Blended, Label Consistent, SIG, Low Frequency, SSBA, Input-aware, WaNet
- 9 Backdoor defense methods: FT, Spectral, AC, FP, ABL, NAD, NC, DBD, ANP
Datasets: CIFAR-10, CIFAR100, GTSRB, Tiny ImageNet
Models: PreAct-Resnet18, VGG19, DenseNet-161, MobileNetV3-Large, EfficientNet-B3,

For detailed structure and implementation details, you can refer to detailed_structure.md.

We also provide a public leaderboard of evaluating above backdoor attacks against above backdoor defense methods.

BackdoorBench will be continuously updated to track the lastest advances of backddor learning. The implementations of more backdoor methods, as well as their evaluations are on the way. You are welcome to contribute your backdoor methods to BackdoorBench.

Requirements

You can run the following script to configurate necessary environment


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        sh ./sh/install.sh

Detailed packages:


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    pytorch==1.11
  
  
    
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    torchvision=0.12
  
  
    
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    keras==2.7.0
  
  
    
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    opencv-python==4.5.4.60
  
  
    
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    pandas==1.3.5
  
  
    
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    Pillow==8.4.0
  
  
    
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    scikit-learn==1.0.2
  
  
    
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    scikit-image==0.18.3
  
  
    
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    torch==1.10.0
  
  
    
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    torchaudio==0.10.0
  
  
    
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    torchvision==0.11.1
  
  
    
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    tqdm==4.61.0

## Usage Please first to make a folder for record, all experiment results with save to record folder as default. And make folder for data to put supported datasets.


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    mkdir record
  
  
    
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    mkdir data
  
  
    
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    mkdir data/cifar10
  
  
    
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    mkdir data/cifar100
  
  
    
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    mkdir data/gtsrb
  
  
    
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    mkdir data/tiny

Please note that due to the RAM issue, you may fail training on ImageNet. For ImageNet, please refer to the for_imagenet folder for a low-RAM alternative.

Attack

This is a demo script of running badnets attack on cifar-10


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    python ./attack/badnet_attack.py --yaml_path ../config/attack/badnet/cifar10.yaml --dataset cifar10 --dataset_path ../data --save_folder_name badnet_0_1

After attack you will get a folder with all files saved in ./record/, including attack_result.pt for attack model and backdoored data, which will be used by following defense methods.

If you want to change the attack methods, dataset, save folder location, you should specify both the attack method script in ../attack and the YAML config file to use different attack methods.

The detailed descriptions for each attack may be put into the add_args function in each script.

Defense

This is a demo script of running ac defense on cifar-10 for badnet attack. Before defense you need to run badnet attack on cifar-10 at first. Then you use the folder name as result_file.


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    python ./defense/ac/ac.py --result_file badnet_0_1 --yaml_path ./config/defense/ac/cifar10.yaml --dataset cifar10

If you want to change the defense methods and the setting for defense, you should specify both the attack method script in ../defense and the YAML config file to use different defense methods.

Supported attacks

	File name	Paper
BadNets	badnets_attack.py	BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain IEEE Access 2019
Blended	blended_attack.py	Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning Arxiv 2017
Label Consistent	lc_attack.py	Label-Consistent Backdoor Attacks Arxiv 2019
SIG	sig_attack.py	A new backdoor attack in cnns by training set corruption ICIP 2019
Low Frequency	lf_attack.py	Rethinking the Backdoor Attacks’ Triggers: A Frequency Perspective ICCV2021
SSBA	ssba_attack.py	Invisible Backdoor Attack with Sample-Specific Triggers ICCV 2021
Input-aware	inputaware_attack.py	Input-Aware Dynamic Backdoor Attack NeurIPS 2020
WaNet	wanet_attack.py	WaNet -- Imperceptible Warping-Based Backdoor Attack ICLR 2021

For SSBA, the file we used with 1-bit embedded in the images is given at https://drive.google.com/drive/folders/1QU771F2_1mKgfNQZm3OMCyegu2ONJiU2?usp=sharing .

For LC attack the file we used is at https://drive.google.com/drive/folders/1Qhj5vXX7kX74IWdrQDwguWsV8UvJmzF4 .

For LF attack the file we used is at https://drive.google.com/drive/folders/16JrANmjDtvGc3lZ_Cv4lKEODFjRebmvk .

Supported defenses

	File name	Paper
FT	ft.py	standard fine-tuning
FP	fp.py	Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks RAID 2018
NAD	nad.py	Neural Attention Distillation: Erasing Backdoor Triggers From Deep Neural Networks ICLR 2021
NC	nc.py	Neural Cleanse: Identifying And Mitigating Backdoor Attacks In Neural Networks, IEEE S&P 2019
ANP	anp.py	Adversarial Neuron Pruning Purifies Backdoored Deep Models NeurIPS 2021
AC	ac.py	Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering ceur-ws 2018
Spectral	spectral.py	Spectral Signatures in Backdoor Attacks NeurIPS 2018
ABL	abl.py	Anti-Backdoor Learning: Training Clean Models on Poisoned Data NeurIPS 2021
DBD	dbd.py	Backdoor Defense Via Decoupling The Training Process ICLR 2022

Copyright

This repository is licensed by The Chinese University of Hong Kong, Shenzhen and Shenzhen Research Institute of Big Data under Creative Commons Attribution-NonCommercial 4.0 International Public License (identified as CC BY-NC-4.0 in SPDX). More details about the license could be found in LICENSE.

This project is built by the Secure Computing Lab of Big Data (SCLBD) at The Chinese University of Hong Kong, Shenzhen and Shenzhen Research Institute of Big Data, directed by Professor Baoyuan Wu. SCLBD focuses on research of trustworthy AI, including backdoor learning, adversarial examples, federated learning, fairness, etc.

If any suggestion or comment, please contact us at wubaoyuan@cuhk.edu.cn.