Edita Grolman — accepted AI-conference papers

2026

AntiStyler: Defending Object Detection Models Against Adversarial Patch Attacks Using Style Removal

Idan Yankelev, Edita Grolman, Yarin Yerushalmi Levi, Amit Giloni, Omer Hofman, Toshiya Shimizu +2

CVPR 2026

Adversarial patch attacks pose a significant threat to the reliability of object detection (OD) models, particularly in real-time security applications. Although several defenses have been proposed, they often suffer from two limitations: 1) reduced performance on benign images, and 2) impractical p

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2025

KDAT: Inherent Adversarial Robustness via Knowledge Distillation with Adversarial Tuning for Object Detection Models

Yarin Yerushalmi Levi, Edita Grolman, Idan Yankelev, Amit Giloni, Omer Hofman, Toshiya Shimizu +2

AAAI 2025technical

Adversarial patches pose a significant threat to computer vision models' integrity, decreasing the accuracy of various tasks, including object detection (OD). Most existing OD defenses exhibit a trade-off between enhancing the model's adversarial robustness and maintaining its performance on benign…

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