BackdoorBench: a comprehensive benchmark of backdoor attack and defense methods

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.

News: (2022/09/17) The manusript of the current BackdoorBench has been accepted to NeurIPS 2022 Track Datasets and Benchmarks. The arxiv version can be found at here.

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Table of Contents

• Requirements

• Usage

  • Attack

  • Defense

• Supported attacks

• Supported defenses

• Results

• Citation

• Copyright

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Requirements

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You can run the following script to configurate necessary environment

sh ./sh/install.sh

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.

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

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

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

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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.

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

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	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

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	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

Results

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We present partial results on cifar10 with poison ratio = 1% here as an example. For complete results, please refer to our leaderboard.

	BackdoorDefense→	Nodefense	Nodefense	Nodefense	FT	FT	FT	FP	FP	FP	NAD	NAD	NAD	NC	NC	NC	ANP	ANP	ANP	AC	AC	AC	Spectral	Spectral	Spectral	ABL	ABL	ABL	DBD	DBD	DBD
TargetedModel	BackdoorAttack↓	C-Acc (%)	ASR (%)	R-Acc (%)	C-Acc (%)	ASR (%)	R-Acc (%)	C-Acc (%)	ASR (%)	R-Acc (%)	C-Acc (%)	ASR (%)	R-Acc (%)	C-Acc (%)	ASR (%)	R-Acc (%)	C-Acc (%)	ASR (%)	R-Acc (%)	C-Acc (%)	ASR (%)	R-Acc (%)	C-Acc (%)	ASR (%)	R-Acc (%)	C-Acc (%)	ASR (%)	R-Acc (%)	C-Acc (%)	ASR (%)	R-Acc (%)
PreAct-Resnet18	BadNets	91.32	95.03	4.67	89.96	1.48	89.39	91.31	57.13	41.62	89.87	2.14	88.71	89.05	1.27	89.16	84.17	0.73	84.04	88.8	86.23	13.28	89.98	92.41	7.2	83.32	0	89.02	89.65	1.28	89.17
PreAct-Resnet18	Blended	93.47	99.92	0.08	92.78	96.11	3.57	93.17	99.26	0.73	92.17	97.69	2.14	93.47	99.92	0.08	86.58	2.19	61.17	88.52	99.72	0.28	90.35	99.84	0.14	77.3	0.73	64.52	69.91	99.98	0.02
PreAct-Resnet18	LC	84.59	99.92	0.07	90.6	11.53	59.74	89.53	0.46	13.32	90.42	10.5	59.7	90.28	6.87	62.24	83.98	4.12	54.69	81.28	98.78	0.86	84.13	99.53	0.42	65.31	0	52.69	61.71	0	54.01
PreAct-Resnet18	SIG	84.48	98.27	1.72	90.8	2.37	69.74	89.1	26.2	20.61	90.02	10.66	64.2	84.48	98.27	1.72	77.97	0.01	48.8	82.41	94.61	5.17	83.01	92.27	7.4	57.8	0	59.97	60.67	100	0
PreAct-Resnet18	LF	93.19	99.28	0.71	92.37	78.44	19.42	92.9	98.97	1.02	92.37	47.83	47.49	91.62	1.41	87.48	85.15	0.6	82.72	88.92	98.09	1.72	92.58	98.67	1.28	81.72	0.21	84.81	50.98	99.7	0.08
PreAct-Resnet18	SSBA	92.88	97.86	1.99	92.14	74.79	23.31	92.54	83.5	15.36	91.91	77.4	20.86	90.99	0.58	87.04	86.54	0.39	79.87	90	96.23	3.53	89.63	90.5	8.71	80.79	0	84.66	63.5	99.51	0.39
PreAct-Resnet18	Input-aware	90.67	98.26	1.66	93.12	1.72	90.53	91.74	0.04	44.54	93.18	1.68	91.12	92.61	0.76	90.87	90.36	1.64	86.99	91.48	88.62	10.61	91.39	90.43	9.13	63.19	90.16	5.77	78.6	8.54	75.28
PreAct-Resnet18	WaNet	91.25	89.73	9.76	93.48	17.1	78.29	91.46	1.09	69.73	93.17	22.98	72.69	91.8	7.53	85.09	85.73	0.43	86.5	91.93	96.8	3.06	91.94	90.17	9.37	83.19	0	85.86	80.9	6.61	77.61
VGG19	BadNets	89.36	95.93	3.81	88.08	25.42	69.68	89.23	92.61	6.82	87.51	38.17	58.3	87.86	1	88.01	NA	NA	NA	86.25	94.37	5.17	88.07	91.67	7.78	85.13	93.92	5.5	10	100	0
VGG19	Blended	90.17	99.12	0.82	88.76	94.37	5.22	90.07	99.11	0.82	88.35	93.08	6.33	85.92	1.79	74.13	NA	NA	NA	87.58	98.13	1.76	89.18	99.76	0.23	88.61	99.48	0.46	10	100	0
VGG19	LC	82.21	97.46	1.78	85.85	7.41	56.07	85.35	96.84	2.29	85.34	6.43	56.57	82.21	97.46	1.78	NA	NA	NA	77.89	35.71	39.92	80.65	73.09	18.9	80.73	98.11	1.21	10	0	11.1
VGG19	SIG	81.69	99.8	0.12	86.89	98.16	1.68	84.52	99.93	0.07	86.01	99.18	0.77	81.69	99.8	0.12	NA	NA	NA	79.6	99.94	0.06	80.27	97.77	1.89	80.53	99.81	0.16	10	0	11.11
VGG19	LF	88.94	93.93	5.62	87.88	62.84	33.01	88.98	91.8	7.46	87.85	59.38	35.01	85.35	9.99	72.79	NA	NA	NA	83.07	89.39	8.59	87.88	92.83	6.44	87.89	93.17	6	10	100	0
VGG19	SSBA	89.48	91.86	7.29	88.2	46.28	47.83	89.4	89.66	9.22	87.65	37.54	54.58	89.48	91.86	7.29	NA	NA	NA	88.62	92.26	6.98	89.61	92.6	6.81	87.66	8.69	78.41	10	0	11.11
VGG19	Input-aware	77.69	94.59	4.79	74.74	13.39	57.97	78.62	86.77	11.79	75.7	23.36	54.71	77.67	94.58	4.79	NA	NA	NA	83.52	82.57	15.2	87.22	84.71	13.58	88.3	86.48	12	10	100	0
VGG19	WaNet	88.43	88.9	10.3	91.03	30.04	64.93	89.61	73.39	24.57	90.82	44.93	51.18	88.43	88.89	10.3	NA	NA	NA	87.96	92.56	7.02	87.76	96.24	3.53	86.29	91.77	7.22	10	100	0
EfficientNet-B3	BadNets	57.48	9.51	55.59	53.69	5.09	52.96	57.97	3.88	57.46	55.29	4.5	54.68	57.48	9.53	55.58	57.48	9.53	55.58	39.76	17.28	36.51	37.64	15.5	36.11	36.44	8.51	36.29	54.44	94.26	3.91
EfficientNet-B3	Blended	65.11	87.76	8.01	61.16	8.6	44.66	65.21	59.03	24.14	62.76	11.86	46.32	59.93	3.87	51.8	65.11	87.76	8.01	34.58	63.32	15.88	53.68	70.81	17.58	18.14	1.91	15.51	52.1	99.94	0.04
EfficientNet-B3	LC	62.09	1.61	47.72	60.68	7.34	42.04	64.07	7.02	44.89	62.51	7.22	42.8	57.32	7.1	41.63	62.09	1.61	47.73	26.55	0	28.11	45.53	0.04	42.14	22.42	0.03	23.32	60.13	0	53.02
EfficientNet-B3	SIG	58.14	99.71	0.13	56.92	3.98	24.03	58.83	0.12	10.84	58.27	3.63	25.83	58.15	99.71	0.13	58.15	99.71	0.13	46.07	99.16	0.26	52.64	98.14	0.76	11.79	9.58	32.02	45.49	99.96	0.04
EfficientNet-B3	LF	56.95	80.06	10.51	53.91	26.43	31.87	56.71	64.11	18.17	55.09	34.42	30.13	52.09	16.62	37.49	53.01	67.98	15.43	34.42	72.51	9.41	52.27	68.96	15.03	41.86	32.03	32.39	50.48	99.21	0.58
EfficientNet-B3	SSBA	55.76	24.4	45.5	53.42	7.1	49.32	56.49	6.07	52.97	54.7	6.91	50.72	55.77	24.4	45.49	55.77	24.4	45.49	29.94	29.13	25.88	49.67	13.8	45.66	27.37	15.74	24.19	51.82	99.27	0.59
EfficientNet-B3	Input-aware	70.01	94.96	2.34	67.84	50.37	17.97	65.61	7.03	15.66	70.3	48.93	22.64	67.47	54.81	8.14	70.01	29.92	27.78	47.2	99.39	0.07	42.22	98.13	0.26	17.3	16.47	17.13	54.13	100	0
EfficientNet-B3	WaNet	71.73	7.93	69.33	70.8	3.59	67.96	25.27	7.43	26.2	73.23	3.14	70.88	71.74	7.93	69.33	71.74	7.93	69.33	37.78	14.34	35.28	52.44	7.11	51.91	37.97	7.66	35.89	58.83	18.44	54.87
MobileNetV3-Large	BadNets	82.45	93.39	5.84	77.99	3	77	82.36	93.97	5.32	79.02	4.57	76.22	75.19	2.53	74.62	82.04	3.72	80.87	70.46	89.98	7.77	78.12	92.83	5.93	76.57	83.59	13.33	57.21	86.84	9.67
MobileNetV3-Large	Blended	83.43	98.76	1.13	78.94	12.27	53.93	83.13	1.46	19.52	79.88	18.81	52.72	77.59	3.53	65.72	75.56	95.02	3.97	71.48	95.08	3.33	79.18	98.16	1.46	76	79.02	15.63	51.67	99.64	0.31
MobileNetV3-Large	LC	75.53	98.67	0.84	76.39	6.47	49.82	80.78	0.12	11.78	78.13	5.92	50.24	76	6.64	48.94	70.21	69.12	14.1	65.83	95.94	2.13	73.47	98.62	0.86	66.66	99.98	0.02	56.71	0	49.44
MobileNetV3-Large	SIG	77.13	98.7	1.04	78.09	2.51	42.93	82	0	17.41	79.1	1.87	37.86	77.13	98.7	1.04	73.49	78.3	14.4	67.85	99.33	0.46	73.47	97.99	1.33	63.2	90.66	5.63	48.42	100	0
MobileNetV3-Large	LF	82.73	96.63	1.77	77.92	24.54	44.76	82.22	0.6	13.33	78.98	10.08	57	82.73	96.63	1.77	75.45	5.44	58.76	72.37	96.33	2.32	79.61	96.58	2.42	75.32	64.92	25.42	51.4	99.37	0.49
MobileNetV3-Large	SSBA	81.84	85.06	12.16	77.8	6.94	68.71	82.14	62.99	29.57	78.35	9.51	68.06	75.95	3.96	71.43	76.26	58.59	30.71	67.79	57.71	29.01	76.85	72.18	21.61	76.22	31.27	50.26	53.93	97.93	1.51
MobileNetV3-Large	Input-aware	77.35	89.17	9.12	78.2	7.09	62.23	79.8	3.32	57.37	79.82	4.46	64.8	76.77	4.37	67.01	77.35	89.17	9.12	71.36	69.29	21.37	78.58	75.03	19.74	73.96	78.38	15.32	50.01	99.98	0.01
MobileNetV3-Large	WaNet	78	70.88	24.8	80.4	4.14	77.22	24.71	9.89	20.82	81.81	3.56	78.97	78	70.88	24.8	78.57	4.71	76.3	69.61	43.82	41.47	77.59	53.23	36.92	59.83	58.71	29.79	58.66	12.3	54.99
DenseNet-161	BadNets	84.33	89.68	9.1	83.59	32.7	56.87	85.16	87.51	10.81	82.28	68.73	26.47	83.1	1.84	82.36	76.53	0.29	79.92	73.17	79.32	16.6	81.13	87.71	10.59	76.32	1.02	79.38	67.41	15.23	64.12
DenseNet-161	Blended	86.37	98.79	1.04	84.39	81.83	14.74	85.95	98.98	0.88	83.69	72.82	19.6	83.17	1.01	72.9	78.93	2.06	65.93	75.45	94.31	4.23	82.06	97.01	2.44	67.81	2.16	73.28	56.66	99.53	0.4
DenseNet-161	LC	78.68	78.06	13.88	83.58	9.12	52.66	83.91	41.49	36.56	82.34	8.68	52.36	81.45	7.33	52.31	80.16	20.92	47.42	68.92	15.27	45.64	77.02	40.88	36.48	58.79	19.9	39.38	72.36	0	60.77
DenseNet-161	SIG	78.63	98.67	1.23	83.49	19.3	49.21	83.23	27.57	38.66	82.7	18.47	48.69	81.62	7.99	43.91	74.25	2.64	45.37	69.85	98.34	1.33	75.61	98.71	1.18	43.47	0	60.47	45.4	96.77	3.1
DenseNet-161	LF	84.32	91.7	6.9	83.07	62.07	31.2	83.93	92.04	6.42	82.51	62.29	30.13	81.11	10.73	67.6	78.5	6.73	70.53	72.91	83.24	12.26	80.61	88.47	9.2	64.4	23.01	53.82	59.62	98.29	1.46
DenseNet-161	SSBA	84.18	84.13	13.39	83.76	32.84	55.9	84.26	83.76	13.49	82.8	29.79	57.22	81.68	8.49	71.78	76.82	1.23	74.62	71.94	60.54	29.57	79.68	75.13	20.34	68.13	0.59	77.33	67.25	97.62	1.93
DenseNet-161	Input-aware	84.46	94.41	5.18	86.77	2.69	82.02	85.15	22.34	60.34	86.47	10.31	76.64	84.45	94.41	5.18	84.03	4.06	75.08	74.19	89.66	7.73	81.64	91.82	6.68	63.79	13.14	51.26	74.12	77.45	17.14
DenseNet-161	WaNet	84.61	73.81	22.72	87.52	2.34	84.48	85.72	24.61	64.71	87.3	6.26	81.16	87.1	1.86	85.07	84.21	1.24	83.69	72.07	41.99	43.37	80.04	65.82	28.01	30.37	97.74	1.58	65.25	10.98	62

Citation

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If interested, you can read our recent works about backdoor learning, and more works about trustworthy AI can be found here.

@inproceedings{wu2022backdoorbench,
  title={BackdoorBench: A Comprehensive Benchmark of Backdoor Learning},
  author={Wu, Baoyuan and Chen, Hongrui and Zhang, Mingda and Zhu, Zihao and Wei, Shaokui and Yuan, Danni and Shen, Chao and Zha, Hongyuan},
  journal={NeurIPS 2022 Track Datasets and Benchmarks},
  year={2022}
}

@inproceedings{wu2022backdoordefense,
  title={Effective Backdoor Defense by Exploiting Sensitivity of Poisoned Samples},
  author={Chen, Weixin and Wu, Baoyuan and Wang, Haoqian},
  booktitle={Neural Information Processing Systems},
  year={2022}
}

@inproceedings{dbd-backdoor-defense-iclr2022,
  title={Backdoor Defense via Decoupling the Training Process},
  author={Huang, Kunzhe and Li, Yiming and Wu, Baoyuan and Qin, Zhan and Ren, Kui},
  booktitle={International Conference on Learning Representations},
  year={2022}
}

@inproceedings{ssba-backdoor-attack-iccv2021,
  title={Invisible backdoor attack with sample-specific triggers},
  author={Li, Yuezun and Li, Yiming and Wu, Baoyuan and Li, Longkang and He, Ran and Lyu, Siwei},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={16463--16472},
  year={2021}
}

@article{gao2022imperceptible,
  title={Imperceptible and Robust Backdoor Attack in 3D Point Cloud},
  author={Gao, Kuofeng and Bai, Jiawang and Wu, Baoyuan and Ya, Mengxi and Xia, Shu-Tao},
  journal={arXiv preprint arXiv:2208.08052},
  year={2022}
}

Copyright

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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 [email protected].