1 citations · 1 across the 6 of their papers we have counts for
8 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…
FM-ChangeNet: Learning Change through Pathwise Feature Transport
Roie Kazoom, George Leifman, Genady Beryozkin
We present FM-ChangeNet, a pathwise-supervised framework for change detection that reformulates bi-temporal reasoning as continuous transport in feature space rather than static en…
RSRCC: A Remote Sensing Regional Change Comprehension Benchmark Constructed via Retrieval-Augmented Best-of-N Ranking
Roie Kazoom, Yotam Gigi, George Leifman +2
Traditional change detection identifies where changes occur, but does not explain what changed in natural language. Existing remote sensing change captioning datasets typically des…
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…
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…
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…
Don't Lag, RAG: Training-Free Adversarial Detection Using RAG
Roie Kazoom, Raz Lapid, Moshe Sipper +1
Adversarial patch attacks pose a major threat to vision systems by embedding localized perturbations that mislead deep models. Traditional defense methods often require retraining…
Enhancing Object Detection Robustness: Detecting and Restoring Confidence in the Presence of Adversarial Patch Attacks
Roie Kazoom, Raz Birman, Ofer Hadar
The widespread adoption of computer vision systems has underscored their susceptibility to adversarial attacks, particularly adversarial patch attacks on object detectors. This stu…