Object detection is considered as one of the most important applications of deep learning.
However, the object detection techniques lose their effectiveness and reliability when they …

Object detection is an important computer vision task with plenty of real-world applications;
therefore, how to enhance its robustness against adversarial attacks has emerged as a …

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 …

Object detection is an important vision task and has emerged as an indispensable
component in many vision system, rendering its robustness as an increasingly important …

Deep neural networks based object detection models have revolutionized computer vision
and fueled the development of a wide range of visual recognition applications. However …

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 …

Deep neural network is the main research branch in artificial intelligence and suitable for
many decision-making fields. Autonomous driving and unmanned vehicle often depend on …

Despite the remarkable success of deep neural networks on many visual tasks, they have
been proved to be vulnerable to adversarial examples. For visual tasks, adversarial …

Many recent studies have shown that deep neural models are vulnerable to adversarial
samples: images with imperceptible perturbations, for example, can fool image classifiers. In …

The recent advances in adversarial attack techniques have witnessed the success of
attacking high-quality CNN-based object detectors. However, in literature, the adversarial …