@inproceedings{Yun26FAB,title={Backdooring Acoustic Foundation Models for Physically Realizable Triggers},author={Yun, Zebin and Ronen, Eyal and Sharif, Mahmood},booktitle={International Symposium on Research in Attacks, Intrusions and Defenses (RAID)},year={2026},}
2024
AISec
The Ultimate Combo: Boosting Adversarial Example Transferability by Composing Data Augmentations
Zebin Yun, Achi-Or Weingarten, Eyal Ronen, and 1 more author
In ACM Workshop on Artificial Intelligence and Security (AISec), 2024
Deep neural networks are vulnerable to adversarial examples that transfer across models. We systematically study how composing data augmentations affects transferability, exploring 46 augmentation techniques individually and in combination via exhaustive and genetic search. On ImageNet and CIFAR-10 across 18 models, simple color-space augmentations combined with standard ones substantially improve transferability over the state of the art.
@inproceedings{yun2024ultimate,title={The Ultimate Combo: Boosting Adversarial Example Transferability by Composing Data Augmentations},author={Yun, Zebin and Weingarten, Achi-Or and Ronen, Eyal and Sharif, Mahmood},booktitle={ACM Workshop on Artificial Intelligence and Security (AISec)},year={2024},doi={10.1145/3689932.3694769},}
HealthSec
Privacy-Preserving Collaborative Genomic Research: A Real-Life Deployment and Vision
Zahra Rahmani, Nahal Shahini, Nadav Gat, and 7 more authors
In ACM Workshop on Cybersecurity in Healthcare (HealthSec), 2024
@inproceedings{rahmani2024privacy,title={Privacy-Preserving Collaborative Genomic Research: A Real-Life Deployment and Vision},author={Rahmani, Zahra and Shahini, Nahal and Gat, Nadav and Yun, Zebin and Jiang, Yuzhou and Farchy, Ofir and Harel, Yaniv and Chaudhary, Vipin and Ayday, Erman and Sharif, Mahmood},booktitle={ACM Workshop on Cybersecurity in Healthcare (HealthSec)},year={2024},doi={10.1145/3689942.3694747},}