Deep neural networks (DNNs) have found widespread applications in interpreting remote
sensing (RS) imagery. However, it has been demonstrated in previous works that DNNs are …

In light of their remarkable predictive capabilities, artificial intelligence (AI) models driven by
deep learning (DL) have witnessed widespread adoption in the agriculture sector …

Machine learning algorithms are at the forefront of the development of advanced information
systems. The rapid progress in machine learning technology has enabled cutting‐edge …

Abstract Vision Transformers (ViTs) have a radically different architecture with significantly
less inductive bias than Convolutional Neural Networks. Along with the improvement in …

With the unprecedented development of deep learning, autonomous vehicles (AVs) have
achieved tremendous progress nowadays. However, AV supported by DNN models is …

Recent research findings suggest that machine learning models are highly susceptible to
backdoor poisoning attacks. Backdoor poisoning attacks can be easily executed and …

Recently, the realm of deep learning applied to 3D point clouds has witnessed significant
progress, accompanied by a growing concern about the emerging security threats to point …

This paper presents a framework for embedding watermarks into DNN hardware
accelerators. Unlike previous works that have looked at protecting the algorithmic …

Critical infrastructures like the power grid are at risk from increasing cyber threats due to
high penetration of interconnected distributed energy resources (DER). Compromised DER …

Recent research has highlighted the vulnerability of Deep Neural Networks (DNNs) against
data poisoning attacks. These attacks aim to inject poisoning samples into the models' …