hsouri / GDP
Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion
☆11Updated 11 months ago
Alternatives and similar repositories for GDP:
Users that are interested in GDP are comparing it to the libraries listed below
- ☆34Updated last year
- Pytorch ImageNet1k Loader with Bounding Boxes.☆12Updated 3 years ago
- Implementation of the paper "Improving the Accuracy-Robustness Trade-off of Classifiers via Adaptive Smoothing".☆11Updated last year
- On the effectiveness of adversarial training against common corruptions [UAI 2022]☆30Updated 2 years ago
- Code relative to "Adversarial robustness against multiple and single $l_p$-threat models via quick fine-tuning of robust classifiers"☆18Updated 2 years ago
- Code for the paper "A Light Recipe to Train Robust Vision Transformers" [SaTML 2023]☆53Updated 2 years ago
- Code and data for the ICLR 2021 paper "Perceptual Adversarial Robustness: Defense Against Unseen Threat Models".☆55Updated 3 years ago
- A modern look at the relationship between sharpness and generalization [ICML 2023]☆43Updated last year
- Certified robustness "for free" using off-the-shelf diffusion models and classifiers☆38Updated last year
- A simple and efficient baseline for data attribution☆11Updated last year
- Code for the paper "Evading Black-box Classifiers Without Breaking Eggs" [SaTML 2024]☆20Updated 11 months ago
- ICLR 2023 paper "Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness" by Yuancheng Xu, Yanchao Sun, Micah Gold…☆24Updated last year
- ☆16Updated last year
- Implementation of Confidence-Calibrated Adversarial Training (CCAT).☆45Updated 4 years ago
- ☆53Updated last year
- [CVPR 2022] "Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free" by Tianlong Chen*, Zhenyu Zhang*, Yihua Zhang*, Shiyu C…☆26Updated 2 years ago
- [ICLR 2021] "Robust Overfitting may be mitigated by properly learned smoothening" by Tianlong Chen*, Zhenyu Zhang*, Sijia Liu, Shiyu Chan…☆46Updated 3 years ago
- [ICLR 2023, Spotlight] Indiscriminate Poisoning Attacks on Unsupervised Contrastive Learning☆30Updated last year
- ☆24Updated 3 years ago
- ☆65Updated last year
- On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them [NeurIPS 2020]☆35Updated 3 years ago
- Official repo for the paper "Make Some Noise: Reliable and Efficient Single-Step Adversarial Training" (https://arxiv.org/abs/2202.01181)☆25Updated 2 years ago
- Code for the paper "Autoregressive Perturbations for Data Poisoning" (NeurIPS 2022)☆20Updated 6 months ago
- What do we learn from inverting CLIP models?☆53Updated last year
- ☆58Updated last year
- [NeurIPS 2021] “When does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?”☆48Updated 3 years ago
- [ICLR 2022 official code] Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?☆29Updated 3 years ago
- Pytorch implementation of Adversarially Robust Distillation (ARD)☆59Updated 5 years ago
- ☆49Updated 3 years ago
- Pytorch Datasets for Easy-To-Hard☆27Updated 2 months ago