[Guided Diffusion Models for Adversarial Purification](arxiv.org/abs/2205.0746) [Guided Diffusion Model for Adversarial Purification](http://arxiv.org/abs/2106.09667)比较有意思的是,两篇文章都是用diffusion来做对抗样本防御(去噪)的,论文标题就差了一个字(笑),意外碰上了,让我们来看看上交和英伟达的论文...
Lumiere: A space-time diffusion model for video generation Videocrafter1: Open diffusion models for high-quality video generation 5. Robust Learning Data Purification Diffusion Models for Adversarial Purification Adversarial purification with score-based generative models Threat Model-Agnostic Adversarial Defen...
Adversarial purification refers to a class of defense methods that remove adversarial perturbations using a generative model. These methods do not make assumptions on the form of attack and the classification model, and thus can defend pre-existing classifiers against unseen threats. However, their per...
@article{wang2022guided, title={Guided diffusion model for adversarial purification}, author={Wang, Jinyi and Lyu, Zhaoyang and Lin, Dahua and Dai, Bo and Fu, Hongfei}, journal={arXiv preprint arXiv:2205.14969}, year={2022} } What does our work do?
Auto1111 port of NVlab's adversarial purification method that uses the forward and reverse processes of diffusion models to remove adversarial perturbations - GitHub - RGX650/sd-webui-diffpure-SDNEXT: Auto1111 port of NVlab's adversarial purification met
024 (2023-11-27) TFMQ-DM Temporal Feature Maintenance Quantization for Diffusion Models https://arxiv.org/pdf/2311.16503.pdf 025 (2023-11-27) MagicAnimate Temporally Consistent Human Image Animation using Diffusion Model https://arxiv.org/pdf/2311.16498.pdf ...
Adversarial defenseDiffusion modelPurification spaceDeep neural networks (DNNs) have been demonstrated to be vulnerable to adversarial samples and many powerful defense methods have been proposed to enhance the adversarial robustness of DNNs. However, these defenses often require adding regularization terms ...
我们的方法得到了SOTA的robustness和accuracy：我们还提出了一种purification的办法。diffpure通过加噪去噪，...
[140] adversarial purification conditioned on image Score SDE, Improved DDPM, DDIM CIFAR-10, ImageNet, CelebA-HQ Wang et al. [141] semantic image generation conditioned on semantic map DDPM Cityscapes, ADE20K, CelebAMask-HQ Zhou et al. [142] shape generation and completion unconditional, ...
[论文总结] DiffAttack: Evasion Attacks Against Diffusion-Based Adversarial Purification说在前面NIPS 2023,原文链接: https://neurips.cc/virtual/2023/poster/71718官方开源代码, coming soon: https://…