This repository contains a collection of papers and resources on backdoor attacks and backdoor defense in deep learning.
- 📃Survey
- ⚔Backdoor Attacks
- Supervised learning (Image classification)
- Semi-supervised learning
- Self-supervised learning
- Federated learning
- Reinforcement Learning
- Other CV tasks (Object detection, segmentation, point cloud)
- Multimodal models (Visual and Language)
- Diffusion model
- Large language model & other NLP tasks
- Graph Neural Networks
- Theoretical analysis
- 🛡Backdoor Defenses
- Defense for supervised learning (Image classification)
- Defense for semi-supervised learning
- Defense for self-supervised learning
- Defense for reinforcement learning
- Defense for federated learning
- Defense for other CV tasks (Object detection, segmentation)
- Defense for multimodal models (Visual and Language)
- Defense for Large Language model & other NLP tasks
- Defense for diffusion models
- Defense for Graph Neural Networks
- Backdoor for social good
- ⚙Benchmark and toolboxes
Year | Publication | Paper |
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2023 | arXiv | Adversarial Machine Learning: A Systematic Survey of Backdoor Attack, Weight Attack and Adversarial Example |
2022 | TPAMI | Data Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses |
2022 | TNNLS | Backdoor Learning: A Survey |
2022 | IEEE Wireless Communications | Backdoor Attacks and Defenses in Federated Learning: State-of-the-art, Taxonomy, and Future Directions |
2021 | Neurocomputing | Defense against Neural Trojan Attacks: A Survey |
2020 | ISQED | A Survey on Neural Trojans |
Venue | Title |
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ICCV 2023 | Backdoor Learning: Recent Advances and Future Trends |
NeurIPS 2023 | Backdoors in Deep Learning |
Year | Publication | Paper | Code |
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2023 | ICCV 2023 | The Perils of Learning From Unlabeled Data: Backdoor Attacks on Semi-supervised Learning | |
2023 | ICML 2023 | Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning | |
2021 | AAAI 2021 | DeHiB: Deep Hidden Backdoor Attack on Semi-supervised Learning via Adversarial Perturbation | |
2021 | TIFS 2021 | Deep Neural Backdoor in Semi-Supervised Learning: Threats and Countermeasures |
Year | Publication | Paper | Code |
---|---|---|---|
2023 | ICCV 2023 | An Embarrassingly Simple Backdoor Attack on Self-supervised Learning | |
2022 | CVPR2022 | Backdoor Attacks on Self-Supervised Learning |
Year | Publication | Paper | Code |
---|---|---|---|
2023 | NeurIPS 2023 | IBA: Towards Irreversible Backdoor Attacks in Federated Learning | |
2023 | NeurIPS 2023 | A3FL: Adversarially Adaptive Backdoor Attacks to Federated Learning | |
2023 | SIGIR 2023 | Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures | |
2022 | ICML2022 | Neurotoxin: Durable Backdoors in Federated Learning | |
2020 | AISTATS 2020 | How To Backdoor Federated Learning | |
2020 | ICLR 2020 | DBA: Distributed Backdoor Attacks against Federated Learning | |
2020 | NeurIPS 2020 | Attack of the Tails: Yes, You Really Can Backdoor Federated Learning | |
2022 | USS 2022 | FLAME: Taming Backdoors in Federated Learning |
Year | Publication | Paper | Code |
---|---|---|---|
2021 | IJCAI 2021 | BACKDOORL: Backdoor Attack against Competitive Reinforcement Learning |
Year | Publication | Paper | Code |
---|---|---|---|
2023 | NeurIPS 2023 | BadTrack: A Poison-Only Backdoor Attack on Visual Object Tracking | |
2022 | ICLR 2022 | Few-Shot Backdoor Attacks on Visual Object Tracking | |
2022 | MM 2022 | Backdoor Attacks on Crowd Counting | |
2021 | ICCV 2021 | A Backdoor Attack against 3D Point Cloud Classifiers | |
2021 | ICCV 2021 | PointBA: Towards Backdoor Attacks in 3D Point Cloud |
Year | Publication | Paper | Code |
---|---|---|---|
2024 | IEEE SP | Backdooring Multimodal Learning | |
2022 | CVPR2022 | Dual-Key Multimodal Backdoors for Visual Question Answering | |
2022 | ICASSP 2022 | Object-Oriented Backdoor Attack Against Image Captioning | |
2022 | ICLR 2022 | Poisoning and Backdooring Contrastive Learning |
Year | Publication | Paper | Code |
---|---|---|---|
2023 | NeurIPS 2023 | VillanDiffusion: A Unified Backdoor Attack Framework for Diffusion Models | |
2023 | ICCV 2023 | Rickrolling the Artist: Injecting Backdoors into Text Encoders for Text-to-Image Synthesis | |
2023 | CVPR 2023 | How to Backdoor Diffusion Models? | |
2023 | CVPR 2023 | TrojDiff: Trojan Attacks on Diffusion Models with Diverse Targets |
Year | Publication | Paper | Code |
---|---|---|---|
2022 | CCS 2022 | Clean-label Backdoor Attack on Graph Neural Networks | |
2022 | ICMR 2022 | Camouflaged Poisoning Attack on Graph Neural Networks | |
2022 | RAID 2022 | Transferable Graph Backdoor Attack | |
2021 | SACMAT 2021 | Backdoor Attacks to Graph Neural Networks | |
2021 | USS 2021 | Graph Backdoor | |
2021 | WiseML 2021 | Explainability-based Backdoor Attacks Against Graph Neural Network |
Year | Publication | Paper | Code |
---|---|---|---|
2020 | NeurIPS 2020 | On the Trade-off between Adversarial and Backdoor Robustness |
Year | Publication | Paper | Code |
---|---|---|---|
2023 | ICCV 2023 | Beating Backdoor Attack at Its Own Game | |
2023 | USENIX Security 2023 | Towards A Proactive ML Approach for Detecting Backdoor Poison Samples | |
2023 | USENIX Security 2023 | ASSET: Robust Backdoor Data Detection Across a Multiplicity of Deep Learning Paradigms | |
2023 | USENIX Security 2023 | How to Sift Out a Clean Data Subset in the Presence of Data Poisoning? | |
2023 | ICLR 2023 | Towards Robustness Certification Against Universal Perturbations | |
2021 | ICML 2021 | SPECTRE: Defending Against Backdoor Attacks Using Robust Statistics | |
2021 | USENIX Security 2021 | Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection | |
2020 | ICLR 2020 | Robust anomaly detection and backdoor attack detection via differential privacy | |
2019 | IEEE SP | Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks | |
2018 | Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering | ||
2018 | NeurIPS 2018 | Spectral Signatures in Backdoor Attacks |
Year | Publication | Paper | Code |
---|---|---|---|
2023 | CVPR 2023 | Backdoor Defense via Adaptively Splitting Poisoned Dataset | |
2023 | CVPR 2023 | Backdoor Defense via Deconfounded Representation Learning | |
2023 | IEEE SP | RAB: Provable Robustness Against Backdoor Attacks | |
2023 | ICLR 2023 | Towards Robustness Certification Against Universal Perturbations | |
2022 | ICLR 2022 | Backdoor defense via decoupling the training process | |
2022 | NeurIPS 2022 | Effective Backdoor Defense by Exploiting Sensitivity of Poisoned Samples | |
2022 | AAAI 2022 | Certified Robustness of Nearest Neighbors against Data Poisoning and Backdoor Attacks | |
2021 | NeurIPS 2021 | Anti-Backdoor Learning: Training Clean Models on Poisoned Data | |
2021 | AAAI 2021 | Intrinsic Certified Robustness of Bagging against Data Poisoning Attacks | |
2022 | NeurIPS 2022 | BagFlip: A Certified Defense against Data Poisoning |
Year | Publication | Paper | Code |
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Year | Publication | Paper | Code |
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2023 | CVPR 2023 | Detecting Backdoors in Pre-trained Encoders | |
2023 | CVPR 2023 | Defending Against Patch-based Backdoor Attacks on Self-Supervised Learning |
Year | Publication | Paper | Code |
---|---|---|---|
2023 | NeurIPS 2023 | BIRD: Generalizable Backdoor Detection and Removal for Deep Reinforcement Learning | |
2023 | ICCV 2023 | PolicyCleanse: Backdoor Detection and Mitigation for Competitive Reinforcement Learning |
Year | Publication | Paper | Code |
---|---|---|---|
2023 | NeurIPS 2023 | Theoretically Modeling Client Data Divergence for Federated Natural Language Backdoor Defense | |
2023 | NeurIPS 2023 | FedGame: A Game-Theoretic Defense against Backdoor Attacks in Federated Learning | |
2023 | NeurIPS 2023 | Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace Training | |
2023 | ICCV 2023 | Multi-Metrics Adaptively Identifies Backdoors in Federated Learning | |
2023 | ICLR 2023 | FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning |
Year | Publication | Paper | Code |
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2023 | NeurIPS 2023 | Django: Detecting Trojans in Object Detection Models via Gaussian Focus Calibration |
Year | Publication | Paper | Code |
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2023 | NeurIPS 2023 | Robust Contrastive Language-Image Pretraining against Data Poisoning and Backdoor Attacks | |
2023 | ICCV 2023 | CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive Learning | |
2023 | ICCV 2023 | TIJO: Trigger Inversion with Joint Optimization for Defending Multimodal Backdoored Models | |
2023 | CVPR 2023 | Detecting Backdoors in Pre-trained Encoders |
Year | Publication | Paper | Code |
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Year | Publication | Paper | Code |
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Year | Publication | Paper | Code |
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2022 | IJCAI2022 | Membership Inference via Backdooring | |
2022 | NeurIPS 2022 | Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection | |
2018 | USS 2018 | Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring |
Year | Publication | Paper | Code |
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2021 | KDD 2021 | What Do You See?: Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors |
Name | Publication | Paper | Code |
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BackdoorBench | NeurIPS 2022 | BackdoorBench: A Comprehensive Benchmark of Backdoor Learning | |
OpenBackdoor | NeurIPS 2022 | A Unified Evaluation of Textual Backdoor Learning: Frameworks and Benchmarks | |
TrojanZoo | EuroS&P 2022 | TrojanZoo: Towards Unified, Holistic, and Practical Evaluation of Neural Backdoors | |
BackdoorBox | BackdoorBox: An Open-sourced Python Toolbox for Backdoor Attacks and Defenses | ||
BackdoorToolbox |