Machine Learning (ML) models are susceptible to adversarial samples that appear as
normal samples but have some imperceptible noise added to them with the intention of …

As an advanced artificial intelligence technique for solving learning problems, deep learning
(DL) has achieved great success in many real-world applications and attracted increasing …

Convolution Neural Network (CNN) models have gained ground in research activities
particularly in medical images used for Diabetes Retinopathy (DR) detection. X-ray, MRI …

Due to the importance of security, the adversarial attack has become an increasingly
popular area for deep learning, especially the black-box adversarial attack, which can only …

In recent years, trust-boundary protection has become a challenging problem in Industrial
Internet of Things (IIoT) environments. Trust boundaries separate IIoT processes and data …

Large-scale introduction of new energy could effectively alleviate energy shortage and
environmental pollution. However, the uncertainty of wind and solar energy brings serious …

Deep neural networks (DNNs) are susceptible to adversarial examples, which are crafted by
deliberately adding some human-imperceptible perturbations on original images. To explore …

Deep neural networks (DNNs) are vulnerable to adversarial examples, which are crafted by
imposing mild perturbation on clean ones. An intriguing property of adversarial examples is …

Deep learning, as an important topic of artificial intelligence, has been widely applied in
various fields, especially in computer vision applications, such as image classification and …

Adversarial attacks pose a security challenge for deep neural networks, motivating
researchers to build various defense methods. Consequently, the performance of black-box …