High-level representation-guided pixel denoising and adversarial training are independent
solutions to enhance the robustness of CNNs against adversarial attacks by pre-processing …

Deep neural networks (DNNs) are vulnerable to maliciously generated adversarial
examples. These examples are intentionally designed by making imperceptible …

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 …

Adversarial examples, generated by adding small but intentionally imperceptible
perturbations to normal examples, can mislead deep neural networks (DNNs) to make …

Autonomous vehicles increasingly exploit computer-vision-based object detection systems
to perceive environments and make critical driving decisions. To increase the quality of …

Recent work on adversarial learning has mainly focused on neural networks and domains in
which those networks excel, such as computer vision and audio processing. Typically, the …

In the development of advanced driver-assistance systems (ADAS) and autonomous
vehicles, machine learning techniques that are based on deep neural networks (DNNs) …

Machine learning is increasingly critical for analysis of the ever-growing corpora of overhead
imagery. Advanced computer vision object detection techniques have demonstrated great …

The deep neural networks (DNNs) have been adopted in a wide spectrum of applications.
However, it has been demonstrated that their are vulnerable to adversarial examples (AEs) …

Deep vision models have provided new capability across a spectrum of applications in
transportation, manufacturing, agriculture, commerce, and security. However, recent studies …