VITA-Group / Trap-and-Replace-Backdoor-DefenseLinks
[NeurIPS'22] Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork. Haotao Wang, Junyuan Hong, Aston Zhang, Jiayu Zhou, Zhangyang Wang
☆15Updated last year
Alternatives and similar repositories for Trap-and-Replace-Backdoor-Defense
Users that are interested in Trap-and-Replace-Backdoor-Defense are comparing it to the libraries listed below
Sorting:
- [NeurIPS 2022] "Randomized Channel Shuffling: Minimal-Overhead Backdoor Attack Detection without Clean Datasets" by Ruisi Cai*, Zhenyu Zh…☆20Updated 3 years ago
- Codes for reproducing the results of the paper "Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness" published at IC…☆27Updated 5 years ago
- [CVPR 2022] "Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free" by Tianlong Chen*, Zhenyu Zhang*, Yihua Zhang*, Shiyu C…☆27Updated 3 years ago
- ☆19Updated 4 years ago
- Pytorch implementation of NPAttack☆12Updated 5 years ago
- Implementation for <Robust Weight Perturbation for Adversarial Training> in IJCAI'22.☆16Updated 3 years ago
- ☆12Updated 3 years ago
- Backdoor Safety Tuning (NeurIPS 2023 & 2024 Spotlight)☆26Updated 11 months ago
- [ICLR 2023, Spotlight] Indiscriminate Poisoning Attacks on Unsupervised Contrastive Learning☆31Updated last year
- On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them [NeurIPS 2020]☆36Updated 4 years ago
- On the effectiveness of adversarial training against common corruptions [UAI 2022]☆30Updated 3 years ago
- Code relative to "Adversarial robustness against multiple and single $l_p$-threat models via quick fine-tuning of robust classifiers"☆20Updated 2 years ago
- Not All Poisons are Created Equal: Robust Training against Data Poisoning (ICML 2022)☆20Updated 3 years ago
- Helper-based Adversarial Training: Reducing Excessive Margin to Achieve a Better Accuracy vs. Robustness Trade-off☆33Updated 3 years ago
- [ICLR 2021] "Robust Overfitting may be mitigated by properly learned smoothening" by Tianlong Chen*, Zhenyu Zhang*, Sijia Liu, Shiyu Chan…☆47Updated 3 years ago
- Pytorch implementation of Adversarially Robust Distillation (ARD)☆59Updated 6 years ago
- ☆35Updated 4 years ago
- ☆15Updated 2 years ago
- Data-Efficient Backdoor Attacks☆18Updated 3 years ago
- ☆11Updated 3 years ago
- [NeurIPS 2021] “When does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?”☆48Updated 3 years ago
- Official implementation of the CVPR 2022 paper "Backdoor Attacks on Self-Supervised Learning".☆76Updated 2 years ago
- [NeurIPS 2021] Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training☆32Updated 3 years ago
- Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses, NeurIPS Spotlight 2020☆27Updated 4 years ago
- Code for "Neuron Shapley: Discovering the Responsible Neurons"☆27Updated last year
- RAB: Provable Robustness Against Backdoor Attacks☆39Updated 2 years ago
- kyleliang919 / Uncovering-the-Connections-BetweenAdversarial-Transferability-and-Knowledge-Transferabilitycode for ICML 2021 paper in which we explore the relationship between adversarial transferability and knowledge transferability.☆17Updated 2 years ago
- This is the code for semi-supervised robust training (SRT).☆18Updated 2 years ago
- ☆22Updated 3 years ago
- Github repo for One-shot Neural Backdoor Erasing via Adversarial Weight Masking (NeurIPS 2022)☆15Updated 2 years ago