In the past few years, it has become increasingly evident that deep neural networks are not
resilient enough to withstand adversarial perturbations in input data, leaving them …

Adversarial attacks and defenses in machine learning and deep neural network (DNN) have
been gaining significant attention due to the rapidly growing applications of deep learning in …

The studies on adversarial attacks and defenses have greatly improved the robustness of
Deep Neural Networks (DNNs). Most advanced approaches have been overwhelmingly …

Pre-trained language models (PLMs) have recently enabled rapid progress on sentiment
classification under the pre-train and fine-tune paradigm, where the fine-tuning phase aims …

The growing use of media has led to the development of several machine learning (ML) and
natural language processing (NLP) tools to process the unprecedented amount of social …

Authors' addresses: Shreya Goyal, Robert Bosch Centre for Data Science and AI, Indian
Institute of Technology Madras, Bhupat and Jyoti Mehta School of Biosciences,, Chennai …

Modern recommender systems may output considerably different recommendations due to
small perturbations in the training data. Changes in the data from a single user will alter the …

Abstract Machine learning and deep learning models are increasingly susceptible to
adversarial attacks, particularly in critical areas like cybersecurity and Information Disorder …

Healthcare data is highly sensitive and confidential, with strict regulations and laws to
protect patient privacy and security. However, these regulations impede the access of …

The improvement of language model robustness, including successful defense against
adversarial attacks, remains an open problem. In computer vision settings, the stochastic …