10 papers
Learning What to Fail On: Failure-Mode Contextual Bandits for Adversarial Data Curation
Roie Kazoom, Ofir Cohen, Rami Puzis +2
We introduce a failure-aware adversarial retrieval-augmented framework for improving robustness in natural language understanding. Rather than selecting synthetic examples with a f…
Learning QoE from Packet-Level Measurements in Encrypted Video Conferencing Traffic
Michael Sidorov, Ofer Hadar
The quality of the user experience has become one of the most important aspects in todays world, as it directly influences individuals willingness to continue using or abandon a pr…
Multi-Image Super Resolution Framework for Detection and Analysis of Plant Roots
Shubham Agarwal, Ofek Nourian, Michael Sidorov +4
Understanding plant root systems is critical for advancing research in soil-plant interactions, nutrient uptake, and overall plant health. However, accurate imaging of roots in sub…
Seeing Isn't Believing: Context-Aware Adversarial Patch Synthesis via Conditional GAN
Roie Kazoom, Alon Goldberg, Hodaya Cohen +1
Adversarial patch attacks pose a severe threat to deep neural networks, yet most existing approaches rely on unrealistic white-box assumptions, untargeted objectives, or produce vi…
Boundary on the Table: Efficient Black-Box Decision-Based Attacks for Structured Data
Roie Kazoom, Yuval Ratzabi, Etamar Rothstein +1
Adversarial robustness in structured data remains an underexplored frontier compared to vision and language domains. In this work, we introduce a novel black-box, decision-based ad…
VAULT: Vigilant Adversarial Updates via LLM-Driven Retrieval-Augmented Generation for NLI
Roie Kazoom, Ofir Cohen, Rami Puzis +2
We introduce VAULT, a fully automated adversarial RAG pipeline that systematically uncovers and remedies weaknesses in NLI models through three stages: retrieval, adversarial gener…