The adversarial vulnerability of deep neural networks (DNNs) has drawn great attention due
to the security risk of applying these models in real-world applications. Based on …

In recent years, the adversarial vulnerability of deep neural networks (DNNs) has raised
increasing attention. Among all the threat models, no-box attacks are the most practical but …

Machine learning has become prevalent in transforming diverse aspects of our daily lives
through intelligent digital solutions. Advanced disease diagnosis, autonomous vehicular …

Recent advancements in adversarial attack research have seen a transition from white-box
to black-box and even no-box threat models, greatly enhancing the practicality of these …

Numerous works study black-box attacks on image classifiers, where adversaries generate
adversarial examples against unknown target models without having access to their internal …

Face recognition systems based on deep learning have recently demonstrated an
outstanding success in solving complex issues. Yet they turn out to be very vulnerable to …

State-of-the-art source code classification models exhibit excellent task transferability, in
which the source code encoders are first pre-trained on a source domain dataset in a self …

In recent years, diffusion models (DMs) have drawn significant attention for their success in
approximating data distributions, yielding state-of-the-art generative results. Nevertheless …

Diffusion models have demonstrated their great ability to generate high-quality images for
various tasks. With such a strong performance, diffusion models can potentially pose a …

Accurate diagnosis of pediatric posterior fossa tumors (PFTs) is critical for saving lives;
however, the limited number of specialists makes accurate diagnostics scarce. To make the …