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 …

Recent natural language processing (NLP) techniques have accomplished high
performance on benchmark data sets, primarily due to the significant improvement in the …

Large vision-language models (VLMs) such as GPT-4 have achieved unprecedented
performance in response generation, especially with visual inputs, enabling more creative …

Abstract Empirical attacks on Federated Learning (FL) systems indicate that FL is fraught
with numerous attack surfaces throughout the FL execution. These attacks can not only …

With the development of high computational devices, deep neural networks (DNNs), in
recent years, have gained significant popularity in many Artificial Intelligence (AI) …

While counterfactual examples are useful for analysis and training of NLP models, current
generation methods either rely on manual labor to create very few counterfactuals, or only …

Adversarial attacks are carried out to reveal the vulnerability of deep neural networks.
Textual adversarial attacking is challenging because text is discrete and a small perturbation …

Adversarial examples expose the vulnerabilities of natural language processing (NLP)
models, and can be used to evaluate and improve their robustness. Existing techniques of …

Recent studies show that neural natural language processing (NLP) models are vulnerable
to backdoor attacks. Injected with backdoors, models perform normally on benign examples …

Humans have been shown to give contrastive explanations, which explain why an observed
event happened rather than some other counterfactual event (the contrast case). Despite the …