It has been recognized that the data generated by the denoising diffusion probabilistic
model (DDPM) improves adversarial training. After two years of rapid development in …

Recently, deep neural networks (DNNs) promote mainly by network architectures and loss
functions; however, the development of neuron models has been quite limited. In this study …

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

The rising popularity of deep neural networks (DNNs) in computer vision has raised
concerns about their robustness in the real world. Recent works in this field have well …

Computer vision using deep neural networks (DNNs) has brought about seminal changes in
people's lives. Applications range from automotive, face recognition in the security industry …

Abstract Machine learning models are known to be susceptible to adversarial perturbation.
One famous attack is the adversarial patch, a particularly crafted sticker that makes the …

Automated content filtering and moderation is an important tool that allows online platforms
to build striving user communities that facilitate cooperation and prevent abuse …

Adversarial machine learning attacks on video action recognition models is a growing
research area and many effective attacks were introduced in recent years. These attacks …

Entropy minimization (EM) is frequently used to increase the accuracy of classification
models when they're faced with new data at test time. EM is a self-supervised learning …

Deep learning (DL) models have recently been widely used in UAV aerial image semantic
segmentation tasks and have achieved excellent performance. However, DL models are …