论文题目:Adv-Diffusion: Imperceptible Adversarial Face Identity Attack via Latent Diffusion Model 作者名字:Decheng Liu, Xijun Wang, Chunlei Peng, Nannan Wang, Ruiming Hu, Xinbo Gao 论文出处:AAAI 2024 报告嘉宾:刘德成讲者简介:刘德成,华山准聘副教授,硕士生导师,2021年6月于西安电子科技大学获得信息与通信...
如果你想深入了解 AdvUnlearn 框架的技术细节或实验结果,欢迎访问 GitHub 项目页面(https://github.com/OPTML-Group/AdvUnlearn)。 [1] Gandikota R, Materzynska J, Fiotto-Kaufman J, et al. Erasing concepts from diffusion models...
We design two novel adversarial guidance techniques to conduct adversarial sampling in the reverse generation process of diffusion models. These two techniques are effective and stable in generating high-quality, realistic adversarial examples by integrating gradients of the target classifier interpretably. ...
论文名称 AdvDiffuser: Natural Adversarial Example Synthesis with Diffusion Models 作者Chen X, Gao X, Zhao J, et al. 期刊名称 Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023: 4562-4572. 简要摘要 过去的对抗样本研究工作通常涉及固定范数扰动预算,无法捕捉人类感知扰动的方式...
该工作以“Soft Nanoforest of Metal Single Atoms for Free Diffusion Catalysis”为题发表于Science Advances上(DOI:10.1126/sciadv.adq2948)。文章的第一作者为上海交通大学化学化工学院博士后孙炎,上海交通大学邱惠斌教授、刘晰教授、浙江...
Unlearned Diffusion Model Benchmark: https://huggingface.co/spaces/Intel/UnlearnDiffAtk-Benchmark AdvUnlearn 框架:对抗性训练与概念擦除的融合 AdvUnlearn 框架的独特之处在于,它系统性地结合了对抗性训练与概念擦除方法,从而提升模型在对抗恶意输入时的安全性和鲁棒性。传统的对抗性训练主要应用于图像分类任务,...
Unlearned Diffusion Model Benchmark: https://huggingface.co/spaces/Intel/UnlearnDiffAtk-Benchmark AdvUnlearn 框架:对抗性训练与概念擦除的融合 AdvUnlearn 框架的独特之处在于,它系统性地结合了对抗性训练与概念擦除方法,从而提升模型在对抗恶意输入时的安全性和鲁棒性。传统的对抗性训练主要应用于图像分类任务,...
We design two novel adversarial guidance techniques to conduct adversarial sampling in the reverse generation process of diffusion models. These two techniques are effective and stable in generating high-quality, realistic adversarial examples by integrating gradients of the target classifier interpretably. ...
we propose AdvDiffVLM, which uses diffusion models to generate natural, unrestricted and targeted adversarial examples via score matching. Specifically, AdvDiffVLM uses Adaptive Ensemble Gradient Estimation (AEGE) to modify the score during the diffusion model’s reverse generation process, ensuring that...
we propose a unified framework Adv-Diffusion that can generate imperceptible adversarial identity perturbations in the latent space but not the raw pixel space, which utilizes strong inpainting capabilities of the latent diffusion model to generate realistic adversarial images. Specifically, we propose the...