arXiv:2606. 13952v2 Announce Type: replace-cross Abstract: Active Motor Noise Cancellation (AMNC) is a noise-reduction feature shipped in commercial fused deposition modeling (FDM) 3D printers.
By Eric Yocam, Varghese Vaidyan, Micah Flack, Gurcan Comert, Judith L. Mwakalonge
arXiv:2610.09819v1 Announce Type: cross
Abstract: Acoustic foundation models (AFMs) have democratized acoustic applications, enabling powerful models for tasks ranging from speech recognition to spea...
By Zebin Yun, Eyal Ronen, Mahmood Sharif
The paper introduces BiShield-TEE, a bilateral obfuscation framework for on-device large language models that jointly transforms input and output dimensions to create ambiguous solutions, thereby protecting the model from reverse engineering. It identifies a vulnerability in existing unilateral obfuscation methods—called the Solution Determinacy Property (SDP)—which allows attackers to recover the model’s true key and achieve high attack accuracy. Experiments demonstrate that BiShield-TEE limits attack success to the baseline black‑box level while improving inference efficiency on real hardware.
By Zhiyuan Wang, Yuezhi Zhou, Ziqi Zhang, Zhu Zhang, Zhixing Tan, Yongheng Deng
arXiv:2606. 27701v1 Announce Type: cross Abstract: While voice control is rapidly becoming a ubiquitous vector of human-AI communication, the risks facing these systems remain poorly understood.
By Andrew C. Cullen, Neil Marchant, Jiani Xie, Paul Montague, Benjamin I. P. Rubinstein
arXiv:2606. 14658v1 Announce Type: cross Abstract: Artificial Intelligence (AI) is increasingly used to automate a variety of real-world computer vision (CV) applications, such as autonomous vehicle control, facial recognition, and security cameras.
By Nicole Villavicencio-Gardu\~no, Maksim Ekin Eren, Milo Prisbrey, Ben Migliori, Michael Teti
Membership inference attacks (MIAs) threaten the privacy of machine learning (ML) training data by determining whether a sample was used to train a target model. Existing MIAs rely on model outputs, r...