no code implementations • 14 Mar 2024 • Roy Weiss, Daniel Ayzenshteyn, Guy Amit, Yisroel Mirsky
In this paper, we unveil a novel side-channel that can be used to read encrypted responses from AI Assistants over the web: the token-length side-channel.
no code implementations • 13 Mar 2024 • Guy Amit, Abigail Goldsteen, Ariel Farkash
We provide the first systematic review of the vulnerability of fine-tuned large language models to membership inference attacks, the various factors that come into play, and the effectiveness of different defense strategies.
1 code implementation • 13 Nov 2023 • Guy Amit, Mosh Levy, Yisroel Mirsky
In addition, in this work we show that neural networks can be taught to systematically memorize and retrieve specific samples from datasets.
no code implementations • 5 Dec 2022 • Alon Zolfi, Guy Amit, Amit Baras, Satoru Koda, Ikuya Morikawa, Yuval Elovici, Asaf Shabtai
In this research, we propose YolOOD - a method that utilizes concepts from the object detection domain to perform OOD detection in the multi-label classification task.
2 code implementations • 23 Aug 2022 • Mosh Levy, Guy Amit, Yuval Elovici, Yisroel Mirsky
By leveraging a set of diverse surrogate models, our method can predict transferability of adversarial examples.
no code implementations • 21 Jan 2022 • Moshe Levy, Guy Amit, Yuval Elovici, Yisroel Mirsky
Deep learning has shown great promise in the domain of medical image analysis.
2 code implementations • 11 Sep 2020 • Yushi Cao, David Berend, Palina Tolmach, Guy Amit, Moshe Levy, Yang Liu, Asaf Shabtai, Yuval Elovici
One of the main causes of unfair behavior in age prediction methods lies in the distribution and diversity of the training data.
1 code implementation • 16 Aug 2020 • Guy Amit, Moshe Levy, Ishai Rosenberg, Asaf Shabtai, Yuval Elovici
Deep neural networks (DNNs) perform well at classifying inputs associated with the classes they have been trained on, which are known as in distribution inputs.
Out-of-Distribution Detection Out of Distribution (OOD) Detection
no code implementations • 6 Feb 2020 • Guy Amit, Ishai Rosenberg, Moshe Levy, Ron Bitton, Asaf Shabtai, Yuval Elovici
In many cases, neural network classifiers are likely to be exposed to input data that is outside of their training distribution data.