2 citations · 4 across the 10 of their papers we have counts for
5 papers · 1 filter
General and Domain-Specific Zero-shot Detection of Generated Images via Conditional Likelihood
Roy Betser, Omer Hofman, Roman Vainshtein +1
The rapid advancement of generative models, particularly diffusion-based methods, has significantly improved the realism of synthetic images. As new generative models continuously…
Training-Free Policy Violation Detection via Activation-Space Whitening in LLMs
Oren Rachmil, Avishag Shapira, Roy Betser +5
As organizations increasingly deploy LLMs in sensitive domains such as legal, financial, and medical settings, ensuring alignment with internal organizational policies has become a…
MAPS: A Multilingual Benchmark for Agent Performance and Security
Omer Hofman, Jonathan Brokman, Oren Rachmil +7
Agentic AI systems, which build on Large Language Models (LLMs) and interact with tools and memory, have rapidly advanced in capability and scope. Yet, since LLMs have been shown t…
Identifying Memorization of Diffusion Models through -Laplace Analysis: Estimators, Bounds and Applications
Jonathan Brokman, Itay Gershon, Amit Giloni +4
Diffusion models, today's leading image generative models, estimate the score function, i.e. the gradient of the log probability of (perturbed) data samples, without direct access…
Manifold Induced Biases for Zero-shot and Few-shot Detection of Generated Images
Jonathan Brokman, Amit Giloni, Omer Hofman +3
Distinguishing between real and AI-generated images, commonly referred to as 'image detection', presents a timely and significant challenge. Despite extensive research in the (semi…