Adversarial attacks have stimulated research interests in the field of deep learning security.
In terms of autonomous driving technology, instance segmentation can help autonomous …

Object detection is a hot topic in computer vision (CV), and it has many applications in
various security fields. However, many works have demonstrated that neural network-based …

Recent research shows that neural networks models used for computer vision (eg, YOLO
and Fast R-CNN) are vulnerable to adversarial evasion attacks. Most of the existing real …

Vehicles have increasingly deployed object detectors to perceive running conditions, and
deep learning networks have been widely adopted by those detectors. Growing neural …

The deep model is widely used and has been demonstrated to have more hidden security
risks. An adversarial attack can bypass the traditional means of defense. By modifying the …

Adversarial examples that can fool deep neural network (DNN) models in computer vision
present a growing threat. The current methods of launching adversarial attacks concentrate …

Recently, many studies show that deep neural networks (DNNs) are susceptible to
adversarial examples, which are generated by adding imperceptible perturbations to the …

In the fields of deep learning and computer vision, the security of object detection models
has received extensive attention. Revealing the security vulnerabilities resulting from …

Applications such as autonomous vehicles and medical screening use deep learning
models to localize and identify hundreds of objects in a single frame. In the past, it has been …

Deep learning plays a critical role in the applications of artificial intelligence. The trend of
processing images or videos as input data and pursuing execution efficiency in practical …