43 citations · 57 across the 14 of their papers we have counts for
8 papers · 1 filter
Dataset Size Recovery from LoRA Weights
Mohammad Salama, Jonathan Kahana, Eliahu Horwitz +1
Model inversion and membership inference attacks aim to reconstruct and verify the data which a model was trained on. However, they are not guaranteed to find all training samples…
Real-Time Deepfake Detection in the Real-World
Bar Cavia, Eliahu Horwitz, Tal Reiss +1
Recent improvements in generative AI made synthesizing fake images easy; as they can be used to cause harm, it is crucial to develop accurate techniques to identify them. This pape…
Distilling Datasets Into Less Than One Image
Asaf Shul, Eliahu Horwitz, Yedid Hoshen
Dataset distillation aims to compress a dataset into a much smaller one so that a model trained on the distilled dataset achieves high accuracy. Current methods frame this as maxim…
Dreamix: Video Diffusion Models are General Video Editors
Eyal Molad, Eliahu Horwitz, Dani Valevski +5
Text-driven image and video diffusion models have recently achieved unprecedented generation realism. While diffusion models have been successfully applied for image editing, very…
Conffusion: Confidence Intervals for Diffusion Models
Eliahu Horwitz, Yedid Hoshen
Diffusion models have become the go-to method for many generative tasks, particularly for image-to-image generation tasks such as super-resolution and inpainting. Current diffusion…
Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection
Eliahu Horwitz, Yedid Hoshen
Despite significant advances in image anomaly detection and segmentation, few methods use 3D information. We utilize a recently introduced 3D anomaly detection dataset to evaluate…
Image Shape Manipulation from a Single Augmented Training Sample
Yael Vinker, Eliahu Horwitz, Nir Zabari +1
In this paper, we present DeepSIM, a generative model for conditional image manipulation based on a single image. We find that extensive augmentation is key for enabling single ima…
Image Shape Manipulation from a Single Augmented Training Sample
Yael Vinker, Eliahu Horwitz, Nir Zabari +1
In this paper, we present DeepSIM, a generative model for conditional image manipulation based on a single image. We find that extensive augmentation is key for enabling single ima…