37 citations · 42 across the 6 of their papers we have counts for
6 papers
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…
Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Models
Natalie Shapira, Mosh Levy, Seyed Hossein Alavi +5
The escalating debate on AI's capabilities warrants developing reliable metrics to assess machine "intelligence". Recently, many anecdotal examples were used to suggest that newer…
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…
The Security of Deep Learning Defences for Medical Imaging
Moshe Levy, Guy Amit, Yuval Elovici +1
Deep learning has shown great promise in the domain of medical image analysis. Medical professionals and healthcare providers have been adopting the technology to speed up and enha…
Fair and accurate age prediction using distribution aware data curation and augmentation
Yushi Cao, David Berend, Palina Tolmach +5
Deep learning-based facial recognition systems have experienced increased media attention due to exhibiting unfair behavior. Large enterprises, such as IBM, shut down their facial…
GIM: Gaussian Isolation Machines
Guy Amit, Ishai Rosenberg, Moshe Levy +3
In many cases, neural network classifiers are likely to be exposed to input data that is outside of their training distribution data. Samples from outside the distribution may be c…