Recent advancements in deep learning architecture have increased its utility in real-life
applications. Deep learning models require a large amount of data to train the model. In …

In recent years, significant progress has been achieved using deep neural networks (DNNs)
in obtaining human-level performance on various long-standing tasks. With the increased …

Infrared physical adversarial examples are of great significance for studying the security of
infrared AI systems that are widely used in our lives such as autonomous driving. Previous …

Low-light images captured under low light or backlight conditions can suffer from different
types of degradation such as low visibility, strong noise and color distortion. In this paper, to …

Adversarial attacks have been demonstrated to fool the deep classification networks. There
are two key characteristics of these attacks: firstly, these perturbations are mostly additive …

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 …

Although recent deep neural network algorithm has shown tremendous success in several
computer vision tasks, their vulnerability against minute adversarial perturbations has raised …

Recently, convolutional neural networks have been shown to be sensitive to artificially
designed perturbations that are imperceptible to the naked eye. Whether it is image …

The data imbalance problem caused by multi-time scales phenomenon affects the prediction
accuracy, validity and robustness of free calcium oxide (fCaO) content in cement clinker …

Adversarial perturbations aim to modify the image pixels in an imperceptible manner such
that the CNN classifier misclassifies an image, whereas humans can predict the original …