Context: Research at the intersection of cybersecurity, Machine Learning (ML), and Software
Engineering (SE) has recently taken significant steps in proposing countermeasures for …

Modern text classification models are susceptible to adversarial examples, perturbed
versions of the original text indiscernible by humans which get misclassified by the model …

Deep neural networks (DNNs) are an indispensable machine learning tool despite the
difficulty of diagnosing what aspects of a model's input drive its decisions. In countless real …

Current methods for training robust networks lead to a drop in test accuracy, which has led
prior works to posit that a robustness-accuracy tradeoff may be inevitable in deep learning …

Biased associations have been a challenge in the development of classifiers for detecting
toxic language, hindering both fairness and accuracy. As potential solutions, we investigate …

Data augmentation is an important component in the robustness evaluation of models in
natural language processing (NLP) and in enhancing the diversity of the data they are …

State-of-the-art attacks on NLP models lack a shared definition of a what constitutes a
successful attack. We distill ideas from past work into a unified framework: a successful …

Abstract Textual Emotion Analysis (TEA) seeks to extract and assess the emotional states of
users from the text. Various Deep Learning (DL) algorithms have emerged rapidly and …

Up until very recently, inspired by a mass of researches on adversarial examples for
computer vision, there has been a growing interest in designing adversarial attacks for …

Deep neural networks and in particular, deep neural classifiers have become an integral
part of many modern applications. Despite their practical success, we still have limited …