1 citations · 1 across the 5 of their papers we have counts for
6 papers
LeakBoost: Perceptual-Loss-Based Membership Inference Attack
Amit Kravchik Taub, Fred M. Grabovski, Guy Amit +1
Membership inference attacks (MIAs) aim to determine whether a sample was part of a model's training set, posing serious privacy risks for modern machine-learning systems. Existing…
Memory Backdoor Attacks on Neural Networks
Eden Luzon, Guy Amit, Roy Weiss +3
Neural networks are often trained on proprietary datasets, making them attractive attack targets. We present a novel dataset extraction method leveraging an innovative training tim…
Back-in-Time Diffusion: Unsupervised Detection of Medical Deepfakes
Fred Grabovski, Lior Yasur, Guy Amit +1
Recent progress in generative models has made it easier for a wide audience to edit and create image content, raising concerns about the proliferation of deepfakes, especially in h…
What Was Your Prompt? A Remote Keylogging Attack on AI Assistants
Roy Weiss, Daniel Ayzenshteyn, Guy Amit +1
AI assistants are becoming an integral part of society, used for asking advice or help in personal and confidential issues. In this paper, we unveil a novel side-channel that can b…
Transpose Attack: Stealing Datasets with Bidirectional Training
Guy Amit, Mosh Levy, Yisroel Mirsky
Deep neural networks are normally executed in the forward direction. However, in this work, we identify a vulnerability that enables models to be trained in both directions and on…
Transferability Ranking of Adversarial Examples
Mosh Levy, Guy Amit, Yuval Elovici +1
Adversarial transferability in black-box scenarios presents a unique challenge: while attackers can employ surrogate models to craft adversarial examples, they lack assurance on wh…