The prosperity of deep neural networks (DNNs) is largely benefited from open-source
datasets, based on which users can evaluate and improve their methods. In this paper, we …

Recent studies revealed that using third-party models may lead to backdoor threats, where
adversaries can maliciously manipulate model predictions based on backdoors implanted …

We introduce ABACuS, a new low-cost hardware-counterbased RowHammer mitigation
technique that performance-, energy-, and area-efficiently scales with worsening …

We present a novel defense, against backdoor attacks on Deep Neural Networks (DNNs),
wherein adversaries covertly implant malicious behaviors (backdoors) into DNNs. Our …

We introduce ABACuS, a new low-cost hardware-counterbased RowHammer mitigation
technique that performance-, energy-, and area-efficiently scales with worsening …

Deep neural networks (DNNs) are vulnerable to backdoor attacks, where the adversary
manipulates a small portion of training data such that the victim model predicts normally on …

Abstract As Deep Neural Networks (DNNs) are increasingly deployed in safety-critical
scenarios, there is a growing need to address bit-flip errors occurring in hardware, such as …

We introduce a new class of attacks on commercial-scale (human-aligned) language
models that induce jailbreaking through targeted bitwise corruptions in model parameters …

Modern spacecraft are increasingly relying on machine learning (ML). However, physical
equipment in space is subject to various natural hazards, such as radiation, which may …

Neural network object detectors are increasingly being used for aerial video analysis, with a
growing demand for onboard processing on UAVs and other limited resources. However …