Fast Adversarial Training (FAT) not only improves the model robustness b...
Practical object detection application can lose its effectiveness on ima...
Developing a practically-robust automatic speech recognition (ASR) is
ch...
This work develops a data-efficient learning from demonstration framewor...
In a transfer-based attack against Automatic Speech Recognition (ASR)
sy...
Contrastive Language-Image Pre-trained (CLIP) models have zero-shot abil...
Adversarial Training (AT), which is commonly accepted as one of the most...
Due to the vulnerability of deep neural networks (DNNs) to adversarial
e...
Recent advances on Vision Transformers (ViT) have shown that
self-attent...
This paper strives to predict fine-grained fashion similarity. In this
s...
Though it is well known that the performance of deep neural networks (DN...
Deep neural networks have been proved that they are vulnerable to advers...
Adversarial attack is a technique for deceiving Machine Learning (ML) mo...
With the rapid development of facial manipulation techniques, face forge...
Adversarial examples are perturbed inputs which can cause a serious thre...
Deep Neural Networks (DNNs) are known to be vulnerable to the maliciousl...
Recent work has demonstrated that neural networks are vulnerable to
adve...
The task of Language-Based Image Editing (LBIE) aims at generating a tar...