Despite the impressive achievements of Deep Neural Networks (DNNs) in computer vision,
their vulnerability to adversarial attacks remains a critical concern. Extensive research has …

Text style transfer is an important task in natural language generation, which aims to control
certain attributes in the generated text, such as politeness, emotion, humor, and many …

A key challenge in adversarial robustness is the lack of a precise mathematical
characterization of human perception, used in the very definition of adversarial attacks that …

In recent years, deep learning-based image classification models have been extensively
studied in academia and widely applied in industry. However, deep learning is inherently …

Abstract Cross-Domain Few-Shot Learning (CD-FSL) is a recently emerging task that tackles
few-shot learning across different domains. It aims at transferring prior knowledge learned …

Adversarial attacks on thermal infrared imaging expose the risk of related applications.
Estimating the security of these systems is essential for safely deploying them in the real …

Adversarial susceptibility of neural image captioning is still under-explored due to the
complex multi-model nature of the task. We introduce a GAN-based adversarial attack to …

Without access to the training data where a black-box victim model is deployed, training a
surrogate model for black-box adversarial attack is still a struggle. In terms of data, we …

Adversarial attacks can mislead deep neural networks (DNNs) by adding imperceptible
perturbations to benign examples. The attack transferability enables adversarial examples to …

Adversarial training is the most effective method to improve adversarial robustness.
However, it does not explicitly regularize the feature space during training. Adversarial …