AppendixA Code BaseOur code is available on GitHub: https://github.com/IBM/villandiffusionB Mathematical DerivationB.1 Clean Diffusion Model via Numerical ReparametrizationRecall that we have def ined the forward process q(x t |x 0 ) := N(ˆ α(t)x 0 , ˆ β 2 (t)I),t ∈ [T...
While text-to-image diffusion models demonstrate impressive generation capabilities, they also exhibit vulnerability to backdoor attacks, which involve the manipulation of model outputs through malicious triggers. In this paper, for the first time, we propose a comprehensive defense method named T2I...
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KGDM: A Diffusion Model to Capture Multiple Relation Semantics for Knowledge Graph Embedding 文章解读: studyai.com/xueshu/pape 文章链接:(10.1609/aaai.v38i8.28732) DataElixir: Purifying Poisoned Dataset to Mitigate Backdoor Attacks via Diffusion Models 文章解读: studyai.com/xueshu/pape 文章链接:(10...
Many existing adversarial attacks generate , is the first that introduces diffusion models into the adversarial attack field. Extensive experiments conducted across diverse model architectures (CNNs, Transformers, and MLPs), datasets (ImageNet, CUB-200, and Standford Cars), and defense mechanisms unders...
Backdoor Unconditional Diffusion Models with VillanDiffusion Arguments --project: Project name for Wandb --mode: Train or test the model, choice: 'train', 'resume', 'sampling`, 'measure', and 'train+measure' train: Train the model resume: Resume the training measure: Compute the FID and ...
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Model Architecture Pre-training Generation Time Storage Performance Textual Inversion Token embedding fine-tuning No ∼20 min Low Moderate DreamBooth Diffusion weight fine-tuning No ∼6 min High Good ELITE Large scale pre-training Yes ∼4 sec Low Moderate Paper Goal Architecture Method Backbone ...