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 …

Deep neural networks (DNNs)-based natural language processing (NLP) systems are
vulnerable to being fooled by adversarial examples presented in recent studies. Intent …

With the wide applications of deep neural networks (DNNs) in various fields, current
research shows their serious security risks due to the lack of privacy protection. Observing …

The rapid progress in the ease of creating and spreading ultra-realistic media over social
platforms calls for an urgent need to develop a generalizable deepfake detection technique …

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 …

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

Recently, a significant boom has been noticed in the generation of a variety of malicious
examples ranging from adversarial perturbations to common noises to natural adversaries …

The vulnerability of deep neural networks to adversarial perturbations has been widely
perceived in the computer vision community. From a security perspective, it poses a critical …

Adversarial training methods are frequently-used empirical defense methods against
adversarial examples. While many regularization techniques demonstrate effectiveness …

Abstract Machine learning algorithms especially deep neural networks have seen
tremendous growth in their real-world deployment. While these algorithms have to yield high …