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Title: GitHub - SCLBD/BackdoorBench · GitHub

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Description: Contribute to SCLBD/BackdoorBench development by creating an account on GitHub.

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13 Commitshttps://github.com/SCLBD/BackdoorBench/commits/main/
https://github.com/SCLBD/BackdoorBench/commits/main/
analysishttps://github.com/SCLBD/BackdoorBench/tree/main/analysis
analysishttps://github.com/SCLBD/BackdoorBench/tree/main/analysis
attackhttps://github.com/SCLBD/BackdoorBench/tree/main/attack
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backdoorbench_nlphttps://github.com/SCLBD/BackdoorBench/tree/main/backdoorbench_nlp
backdoorbench_nlphttps://github.com/SCLBD/BackdoorBench/tree/main/backdoorbench_nlp
confighttps://github.com/SCLBD/BackdoorBench/tree/main/config
confighttps://github.com/SCLBD/BackdoorBench/tree/main/config
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LICENSEhttps://github.com/SCLBD/BackdoorBench/blob/main/LICENSE
README.mdhttps://github.com/SCLBD/BackdoorBench/blob/main/README.md
README.mdhttps://github.com/SCLBD/BackdoorBench/blob/main/README.md
READMEhttps://github.com/SCLBD/BackdoorBench
Licensehttps://github.com/SCLBD/BackdoorBench
https://github.com/SCLBD/BackdoorBench/blob/main/resource/pyg_logo.png
https://github.com/SCLBD/BackdoorBench#backdoorbench-a-comprehensive-benchmark-of-backdoor-attack-and-defense-methods
https://camo.githubusercontent.com/d5b6a4204ab61ab40a28a832a798c61975b9887038b8fe4793996906f1e6e8cf/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f5079546f7263682d312e31312d627269676874677265656e
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Website https://backdoorbench.github.io/
Paper https://openreview.net/pdf?id=31_U7n18gM7
Doc http://backdoorbench.com/doc/index
Leaderboard http://backdoorbench.com/leader_cifar10
https://github.com/SCLBD/BackdoorBench#model-and-data-updates
save_load_attack.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/utils/save_load_attack.py
Backdoor Modelhttps://cuhko365.sharepoint.com/:f:/s/SDSbackdoorbench/EmYD8BoPY8hAqNCV_Rb_zwsBFdqf88Yx01xi0V8tc4whvw?e=d7oJNc
https://github.com/SCLBD/BackdoorBench#v22-updates
STRIPhttps://github.com/SCLBD/BackdoorBench/blob/main/detection_pretrain/strip.py
BEATRIXhttps://github.com/SCLBD/BackdoorBench/blob/main/detection_pretrain/beatrix.py
SCANhttps://github.com/SCLBD/BackdoorBench/blob/main/detection_pretrain/scan.py
SPECTREhttps://github.com/SCLBD/BackdoorBench/blob/main/detection_pretrain/spectre.py
SShttps://github.com/SCLBD/BackdoorBench/blob/main/detection_pretrain/spectral.py
AGPDhttps://github.com/SCLBD/BackdoorBench/blob/main/detection_pretrain/agpd.py
SentiNethttps://github.com/SCLBD/BackdoorBench/blob/main/detection_infer/sentinet.py
STRIPhttps://github.com/SCLBD/BackdoorBench/blob/main/detection_infer/strip.py
TeCohttps://github.com/SCLBD/BackdoorBench/blob/main/detection_infer/teco.py
herehttps://github.com/SCLBD/BackdoorBench/tree/v2
https://github.com/SCLBD/BackdoorBench#-for-v20-please-check-here
herehttps://github.com/SCLBD/BackdoorBench/tree/v1
https://github.com/SCLBD/BackdoorBench#-for-v10-please-check-here
Featureshttps://github.com/SCLBD/BackdoorBench#features
Installationhttps://github.com/SCLBD/BackdoorBench#Installation
Quick Starthttps://github.com/SCLBD/BackdoorBench#quick-start
Attackhttps://github.com/SCLBD/BackdoorBench#attack
Defensehttps://github.com/SCLBD/BackdoorBench#defense
Supported attackshttps://github.com/SCLBD/BackdoorBench#supported-attacks
Supported defenseshttps://github.com/SCLBD/BackdoorBench#supported-defsense
Analysis Toolshttps://github.com/SCLBD/BackdoorBench#analysis-tools
Citationhttps://github.com/SCLBD/BackdoorBench#citation
Copyrighthttps://github.com/SCLBD/BackdoorBench#copyright
https://github.com/SCLBD/BackdoorBench#features
[Back to top]https://github.com/SCLBD/BackdoorBench#top
BadNetshttps://github.com/SCLBD/BackdoorBench/blob/main/attack/badnet.py
Blendedhttps://github.com/SCLBD/BackdoorBench/blob/main/attack/blended.py
Blindhttps://github.com/SCLBD/BackdoorBench/blob/main/attack/blind.py
BppAttackhttps://github.com/SCLBD/BackdoorBench/blob/main/attack/bpp.py
CTRLhttps://github.com/SCLBD/BackdoorBench/blob/main/attack/ctrl.py
FTrojanhttps://github.com/SCLBD/BackdoorBench/blob/main/attack/ftrojann.py
Input-awarehttps://github.com/SCLBD/BackdoorBench/blob/main/attack/inputaware.py
LChttps://github.com/SCLBD/BackdoorBench/blob/main/attack/lc.py
LFhttps://github.com/SCLBD/BackdoorBench/blob/main/attack/lf.py
LIRAhttps://github.com/SCLBD/BackdoorBench/blob/main/attack/lira.py
PoisonInkhttps://github.com/SCLBD/BackdoorBench/blob/main/attack/poison_ink.py
ReFoolhttps://github.com/SCLBD/BackdoorBench/blob/main/attack/refool.py
SIGhttps://github.com/SCLBD/BackdoorBench/blob/main/attack/sig.py
SSBAhttps://github.com/SCLBD/BackdoorBench/blob/main/attack/ssba.py
TrojanNNhttps://github.com/SCLBD/BackdoorBench/blob/main/attack/trojannn.py
WaNethttps://github.com/SCLBD/BackdoorBench/blob/main/attack/wanet.py
ABLhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/abl.py
AChttps://github.com/SCLBD/BackdoorBench/blob/main/defense/ac.py
ANPhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/anp.py
CLPhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/clp.py
D-BRhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/d-br.py
D-SThttps://github.com/SCLBD/BackdoorBench/blob/main/defense/d-st.py
DBDhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/dbd.py
EPhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/ep.py
BNPhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/bnp.py
FPhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/fp.py
FThttps://github.com/SCLBD/BackdoorBench/blob/main/defense/ft.py
FT-SAMhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/ft-sam.py
I-BAUhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/i-bau.py
MCRhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/mcr.py
NABhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/nab.py
NADhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/nad.py
NChttps://github.com/SCLBD/BackdoorBench/blob/main/defense/nc.py
NPDhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/npd.py
RNPhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/rnp.py
SAUhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/sau.py
SShttps://github.com/SCLBD/BackdoorBench/blob/main/defense/spectral.py
STRIPhttps://github.com/SCLBD/BackdoorBench/blob/main/detection_pretrain/strip.py
BEATRIXhttps://github.com/SCLBD/BackdoorBench/blob/main/detection_pretrain/beatrix.py
SCANhttps://github.com/SCLBD/BackdoorBench/blob/main/detection_pretrain/scan.py
SPECTREhttps://github.com/SCLBD/BackdoorBench/blob/main/detection_pretrain/spectre.py
SShttps://github.com/SCLBD/BackdoorBench/blob/main/detection_pretrain/spectral.py
AGPDhttps://github.com/SCLBD/BackdoorBench/blob/main/detection_pretrain/agpd.py
SentiNethttps://github.com/SCLBD/BackdoorBench/blob/main/detection_infer/sentinet.py
STRIPhttps://github.com/SCLBD/BackdoorBench/blob/main/detection_infer/strip.py
TeCohttps://github.com/SCLBD/BackdoorBench/blob/main/detection_infer/teco.py
public leaderboardhttp://backdoorbench.com/leader_cifar10
https://github.com/SCLBD/BackdoorBench#installation
[Back to top]https://github.com/SCLBD/BackdoorBench#top
linkhttps://github.com/SCLBD/bdzoo2-pip
https://github.com/SCLBD/BackdoorBench#quick-start
https://github.com/SCLBD/BackdoorBench#attack
[Back to top]https://github.com/SCLBD/BackdoorBench#top
default.yamlhttps://github.com/SCLBD/BackdoorBench/blob/main/config/attack/badnet/default.yaml
herehttps://drive.google.com/drive/folders/1lnCObVBIUTSlLWIBQtfs_zi7W8yuvR-2?usp=share_link
https://github.com/SCLBD/BackdoorBench#defense
[Back to top]https://github.com/SCLBD/BackdoorBench#top
default.yamlhttps://github.com/SCLBD/BackdoorBench/blob/main/config/defense/abl/default.yaml
https://github.com/SCLBD/BackdoorBench#supported-attacks
[Back to top]https://github.com/SCLBD/BackdoorBench#top
badnet.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/attack/badnet.py
BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chainhttps://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwir55bv0-X2AhVJIjQIHYTjAMgQFnoECCEQAQ&url=https%3A%2F%2Fmachine-learning-and-security.github.io%2Fpapers%2Fmlsec17_paper_51.pdf&usg=AOvVaw1Cu3kPaD0a4jgvwkPCX63j
blended.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/attack/blended.py
Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoninghttps://arxiv.org/abs/1712.05526v1
blind.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/attack/blind.py
Blind Backdoors in Deep Learning Modelshttps://www.cs.cornell.edu/~shmat/shmat_usenix21blind.pdf
bpp.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/attack/bpp.py
BppAttack: Stealthy and Efficient Trojan Attacks against Deep Neural Networks via Image Quantization and Contrastive Adversarial Learninghttps://openaccess.thecvf.com/content/CVPR2022/papers/Wang_BppAttack_Stealthy_and_Efficient_Trojan_Attacks_Against_Deep_Neural_Networks_CVPR_2022_paper.pdf
ctrl.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/attack/ctrl.py
An Embarrassingly Simple Backdoor Attack on Self-supervised Learninghttps://www.google.com.hk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwiB6pfnu7KDAxWiaPUHHSzeDXIQFnoECAsQAQ&url=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent%2FICCV2023%2Fpapers%2FLi_An_Embarrassingly_Simple_Backdoor_Attack_on_Self-supervised_Learning_ICCV_2023_paper.pdf&usg=AOvVaw2rR9-Se-bZgF3U0EU4puPE&opi=89978449
ftrojann.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/attack/ftrojann.py
An Invisible Black-box Backdoor Attack through Frequency Domainhttps://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136730396.pdf
inputaware.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/attack/inputaware.py
Input-Aware Dynamic Backdoor Attackhttps://proceedings.neurips.cc/paper/2020/file/234e691320c0ad5b45ee3c96d0d7b8f8-Paper.pdf
lc.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/attack/lc.py
Label-Consistent Backdoor Attackshttps://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwjvwKTx2bH4AhXCD0QIHVMWApkQFnoECAsQAQ&url=https%3A%2F%2Farxiv.org%2Fabs%2F1912.02771&usg=AOvVaw0NbPR9lguGTsEn3ZWtPBDR
lf.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/attack/lf.py
Rethinking the Backdoor Attacks’ Triggers: A Frequency Perspectivehttps://openaccess.thecvf.com/content/ICCV2021/papers/Zeng_Rethinking_the_Backdoor_Attacks_Triggers_A_Frequency_Perspective_ICCV_2021_paper.pdf
lira.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/attack/lira.py
LIRA: Learnable, Imperceptible and Robust Backdoor Attackshttps://openaccess.thecvf.com/content/ICCV2021/papers/Doan_LIRA_Learnable_Imperceptible_and_Robust_Backdoor_Attacks_ICCV_2021_paper.pdf
poison_ink.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/attack/poison_ink.py
Poison ink: Robust and invisible backdoor attackhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9870671
refool.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/attack/refool.py
Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networkshttps://www.google.com.hk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwiioOK2vLKDAxU4iq8BHbPGDEoQFnoECAsQAQ&url=https%3A%2F%2Fwww.ecva.net%2Fpapers%2Feccv_2020%2Fpapers_ECCV%2Fpapers%2F123550188.pdf&usg=AOvVaw2_cqNKyWBEfXSBhaW5IOMj&opi=89978449
sig.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/attack/sig.py
A new backdoor attack in cnns by training set corruptionhttps://ieeexplore.ieee.org/document/8802997
ssba.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/attack/ssba.py
Invisible Backdoor Attack with Sample-Specific Triggershttps://openaccess.thecvf.com/content/ICCV2021/papers/Li_Invisible_Backdoor_Attack_With_Sample-Specific_Triggers_ICCV_2021_paper.pdf
trojannn.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/attack/trojannn.py
Trojaning Attack on Neural Networkshttps://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=2782&context=cstech
wanet.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/attack/wanet.py
WaNet -- Imperceptible Warping-Based Backdoor Attackhttps://openreview.net/pdf?id=eEn8KTtJOx
https://github.com/SCLBD/BackdoorBench#supported-defenses
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abl.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/abl.py
Anti-Backdoor Learning: Training Clean Models on Poisoned Datahttps://proceedings.neurips.cc/paper/2021/file/7d38b1e9bd793d3f45e0e212a729a93c-Paper.pdf
ac.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/ac.py
Detecting Backdoor Attacks on Deep Neural Networks by Activation Clusteringhttp://ceur-ws.org/Vol-2301/paper_18.pdf
anp.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/anp.py
Adversarial Neuron Pruning Purifies Backdoored Deep Modelshttps://proceedings.neurips.cc/paper/2021/file/8cbe9ce23f42628c98f80fa0fac8b19a-Paper.pdf
clp.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/clp.py
Data-free backdoor removal based on channel lipschitznesshttps://arxiv.org/pdf/2208.03111.pdf
d-br.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/d-br.py
d-st.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/d-st.py
Effective backdoor defense by exploiting sensitivity of poisoned sampleshttps://proceedings.neurips.cc/paper_files/paper/2022/file/3f9bbf77fbd858e5b6e39d39fe84ed2e-Paper-Conference.pdf
dbd.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/dbd.py
Backdoor Defense Via Decoupling The Training Processhttps://arxiv.org/pdf/2202.03423.pdf
ep.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/ep.py
bnp.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/bnp.py
Pre-activation Distributions Expose Backdoor Neuronshttps://proceedings.neurips.cc/paper_files/paper/2022/file/76917808731dae9e6d62c2a7a6afb542-Paper-Conference.pdf
fp.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/fp.py
Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networkshttps://link.springer.com/chapter/10.1007/978-3-030-00470-5_13
ft.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/ft.py
ft-sam.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/ft-sam.py
Enhancing Fine-Tuning Based Backdoor Defense with Sharpness-Aware Minimizationhttps://www.google.com.hk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwjGlOzGuLKDAxW2j68BHQ1cDKoQFnoECAsQAQ&url=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent%2FICCV2023%2Fpapers%2FZhu_Enhancing_Fine-Tuning_Based_Backdoor_Defense_with_Sharpness-Aware_Minimization_ICCV_2023_paper.pdf&usg=AOvVaw3j_4UcalC7moFEDuHaLXjO&opi=89978449
i-bau.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/i-bau.py
Adversarial unlearning of backdoors via implicit hypergradienthttps://arxiv.org/pdf/2110.03735.pdf
mcr.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/mcr.py
Bridging mode connectivity in loss landscapes and adversarial robustnesshttps://openreview.net/pdf?id=SJgwzCEKwH
nab.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/nab.py
Beating Backdoor Attack at Its Own Gamehttps://www.google.com.hk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwiT1P3LubKDAxWBdvUHHZU0C_4QFnoECAgQAQ&url=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent%2FICCV2023%2Fpapers%2FLiu_Beating_Backdoor_Attack_at_Its_Own_Game_ICCV_2023_paper.pdf&usg=AOvVaw2q9z7lRkjVriRnqJCfacLZ&opi=89978449
nad.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/nad.py
Neural Attention Distillation: Erasing Backdoor Triggers From Deep Neural Networkshttps://openreview.net/pdf?id=9l0K4OM-oXE
nc.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/nc.py
Neural Cleanse: Identifying And Mitigating Backdoor Attacks In Neural Networkshttps://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8835365
npd.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/npd.py
Neural polarizer: A lightweight and effective backdoor defense via purifying poisoned featureshttps://openreview.net/pdf?id=VFhN15Vlkj
rnp.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/rnp.py
Reconstructive Neuron Pruning for Backdoor Defensehttps://proceedings.mlr.press/v202/li23v/li23v.pdf
sau.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/sau.py
Shared adversarial unlearning: Backdoor mitigation by unlearning shared adversarial exampleshttps://openreview.net/pdf?id=zqOcW3R9rd
spectral.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/defense/spectral.py
Spectral Signatures in Backdoor Attackshttps://proceedings.neurips.cc/paper/2018/file/280cf18baf4311c92aa5a042336587d3-Paper.pdf
https://github.com/SCLBD/BackdoorBench#supported-detection
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https://github.com/SCLBD/BackdoorBench#pretrain
strip.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/detection_pretrain/strip.py
STRIP: A Defence Against Trojan Attacks on Deep Neural Networkshttps://dl.acm.org/doi/pdf/10.1145/3359789.3359790
beatrix.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/detection_pretrain/beatrix.py
The Beatrix Resurrections: Robust Backdoor Detection via Gram Matriceshttps://www.google.com.hk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwibhPKPwLKDAxXFia8BHUp2CmEQFnoECCIQAQ&url=https%3A%2F%2Fwww.usenix.org%2Fsystem%2Ffiles%2Fusenixsecurity23-pan.pdf&usg=AOvVaw0GLq_ZUB3OK1AoKhW9TNx_&opi=89978449
scan.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/detection_pretrain/scan.py
Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detectionhttps://www.usenix.org/system/files/sec21-tang-di.pdf
spectre.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/detection_pretrain/spectre.py
SPECTRE: Defending Against Backdoor Attacks Using Robust Statisticshttps://par.nsf.gov/servlets/purl/10268374
spectral.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/detection_pretrain/spectral.py
Spectral Signatures in Backdoor Attackshttps://proceedings.neurips.cc/paper/2018/file/280cf18baf4311c92aa5a042336587d3-Paper.pdf
agpd.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/detection_pretrain/agpd.py
Activation Gradient based Poisoned Sample Detection Against Backdoor Attackshttps://arxiv.org/abs/2312.06230
https://github.com/SCLBD/BackdoorBench#inference-time
sentinet.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/detection_infer/sentinet.py
Sentinet: Detecting localized universal attacks against deep learning systemshttps://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9283822
strip.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/detection_infer/strip.py
STRIP: A Defence Against Trojan Attacks on Deep Neural Networkshttps://dl.acm.org/doi/pdf/10.1145/3359789.3359790
teco.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/detection_infer/teco.py
Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistencyhttps://www.google.com.hk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwjyktXTv7KDAxUZft4KHeeTC5wQFnoECAwQAQ&url=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent%2FCVPR2023%2Fpapers%2FLiu_Detecting_Backdoors_During_the_Inference_Stage_Based_on_Corruption_Robustness_CVPR_2023_paper.pdf&usg=AOvVaw38J5yiw9xqwcRNAyeZB1QF&opi=89978449
https://github.com/SCLBD/BackdoorBench#model-and-data-downloading
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save_load_attack.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/utils/save_load_attack.py
Backdoor Modelhttps://cuhko365.sharepoint.com/:f:/s/SDSbackdoorbench/EmYD8BoPY8hAqNCV_Rb_zwsBFdqf88Yx01xi0V8tc4whvw?e=d7oJNc
https://github.com/SCLBD/BackdoorBench#analysis-tools
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visual_tsne.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/analysis/visual_tsne.py
visual_umap.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/analysis/visual_umap.py
visual_quality.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/analysis/visual_quality.py
visual_na.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/analysis/visual_na.py
visual_shap.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/analysis/visual_shap.py
visual_gradcam.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/analysis/visual_gradcam.py
visualize_fre.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/analysis/visualize_fre.py
visual_act.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/analysis/visual_act.py
visual_fv.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/analysis/visual_fv.py
visual_fm.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/analysis/visual_fm.py
visual_actdist.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/analysis/visual_actdist.py
visual_tac.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/analysis/visual_tac.py
visual_lips.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/analysis/visual_lips.py
visual_landscape.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/analysis/visual_landscape.py
visual_network.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/analysis/visual_network.py
visual_hessian.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/analysis/visual_hessian.py
visual_metric.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/analysis/visual_metric.py
visual_cm.pyhttps://github.com/SCLBD/BackdoorBench/blob/main/analysis/visual_cm.py
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