Abstract Vision Transformers (ViTs) have demonstrated the state-of-the-art performance in
various vision-related tasks. The success of ViTs motivates adversaries to perform backdoor …

Abstract Vision Transformers (ViTs), which made a splash in the field of computer vision
(CV), have shaken the dominance of convolutional neural networks (CNNs). However, in the …

Given the power of vision transformers a new learning paradigm pre-training and then
prompting makes it more efficient and effective to address downstream visual recognition …

Transformer-based architectures have recently exhibited remarkable successes across
different domains beyond just powering large language models. However, existing …

The Transformers architecture has recently emerged as a revolutionary paradigm in the field
of deep learning, particularly excelling in Natural Language Processing (NLP) and …

Bit-Trojan attacks based on Bit-Flip Attacks (BFAs) have emerged as severe threats to Deep
Neural Networks (DNNs) deployed in safety-critical systems since they can inject Trojans …

Recent advances in Transformer architecture have empowered its empirical success in
various tasks across different domains. However, existing works mainly focus on improving …

Spiking Neural Networks (SNNs), the third generation neural networks, are known for their
low energy consumption and high robustness. SNNs are developing rapidly and can …

Vision transformers have achieved impressive performance in various vision-related tasks,
but their vulnerability to backdoor attacks is under-explored. A handful of existing works …

Recent studies have revealed the vulnerability of Deep Neural Network (DNN) models to
backdoor attacks. However, existing backdoor attacks arbitrarily set the trigger mask or use a …