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

Multi-modality image fusion aims to combine different modalities to produce fused images
that retain the complementary features of each modality, such as functional highlights and …

Over the past decade, deep learning has revolutionized conventional tasks that rely on hand-
craft feature extraction with its strong feature learning capability, leading to substantial …

Network binarization emerges as one of the most promising compression approaches
offering extraordinary computation and memory savings by minimizing the bit-width …

Recent studies have shown that adversarial examples hand-crafted on one white box model
can be used to attack other black-box models. Such cross-model transferability makes it …

Artificial Intelligence Generated Content (AIGC) is one of the latest achievements in AI
development. The content generated by related applications, such as text, images and …

Image fusion is a significant problem in many fields including digital photography,
computational imaging and remote sensing, to name but a few. Recently, deep learning has …

Quantization has emerged as an essential technique for deploying deep neural networks
(DNNs) on devices with limited resources. However, quantized models exhibit vulnerabilities …

Despite the impressive performance of Artificial Intelligence (AI) systems, their robustness
remains elusive and constitutes a key issue that impedes large-scale adoption. Besides …

Deep neural networks (DNNs) are shown to be vulnerable to universal adversarial
perturbations (UAP), a single quasi-imperceptible perturbation that deceives the DNNs on …