Enhancing Model Robustness and Accuracy Against Adversarial Attacks via Adversarial Input Training
Ganesh Ingle,
Sanjesh Pawale
Abstract:Adversarial attacks present a formidable challenge to the integrity of Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) models, particularly in the domain of power quality disturbance (PQD) classification, necessitating the development of effective defense mechanisms. These attacks, characterized by their subtlety, can significantly degrade the performance of models critical for maintaining power system stability and efficiency. This study introduces the concept of adversarial attacks on CNN-LSTM… Show more
Set email alert for when this publication receives citations?
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.