5 papers
TARO: Temporal Adversarial Rectification Optimization Using Diffusion Models as Purifiers
Daniel Wesego, Pedram Rooshenas
Adversarial purification with diffusion models seeks to project adversarial examples back toward the data manifold, but balancing semantic preservation and robustness against adapt…
DCInject: Persistent Backdoor Attacks via Frequency Manipulation in Personal Federated Learning
Nahom Birhan, Daniel Wesego, Dereje Shenkut +2
Personalized federated learning (PFL) creates client-specific models to handle data heterogeneity. Previously, PFL has been shown to be naturally resistant to backdoor attack propa…
Graph Representation Learning with Diffusion Generative Models
Daniel Wesego
Diffusion models have established themselves as state-of-the-art generative models across various data modalities, including images and videos, due to their ability to accurately a…
Multimodal ELBO with Diffusion Decoders
Daniel Wesego, Pedram Rooshenas
Multimodal variational autoencoders have demonstrated their ability to learn the relationships between different modalities by mapping them into a latent representation. Their desi…
Score-Based Multimodal Autoencoder
Daniel Wesego, Pedram Rooshenas
Multimodal Variational Autoencoders (VAEs) represent a promising group of generative models that facilitate the construction of a tractable posterior within the latent space given…