activity
20202026
most citedBENN: Bias Estimation Using Deep Neural Network

2 citations · 4 across the 3 of their papers we have counts for

collaborators

8 papers

cs.CR2026

Adversarial Intent is a Latent Variable: Stateful Trust Inference for Securing Multimodal Agentic RAG

Inderjeet Singh, Vikas Pahuja, Aishvariya Priya Rathina Sabapathy +8

Current stateless defences for multimodal agentic RAG fail to detect adversarial strategies that distribute malicious semantics across retrieval, planning, and generation component…

cs.AI2025

Counterfactual-based Agent Influence Ranker for Agentic AI Workflows

Amit Giloni, Chiara Picardi, Roy Betser +3

An Agentic AI Workflow (AAW), also known as an LLM-based multi-agent system, is an autonomous system that assembles several LLM-based agents to work collaboratively towards a share…

cs.CR2025

LumiMAS: A Comprehensive Framework for Real-Time Monitoring and Enhanced Observability in Multi-Agent Systems

Ron Solomon, Yarin Yerushalmi Levi, Lior Vaknin +8

The incorporation of LLMs in multi-agent systems (MASs) has the potential to significantly improve our ability to autonomously solve complex problems. However, such systems introdu…

cs.CV2025

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…

cs.CV2025

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…

cs.LG2024

Addressing Key Challenges of Adversarial Attacks and Defenses in the Tabular Domain: A Methodological Framework for Coherence and Consistency

Yael Itzhakev, Amit Giloni, Yuval Elovici +1

Machine learning models trained on tabular data are vulnerable to adversarial attacks, even in realistic scenarios where attackers only have access to the model's outputs. Since ta…