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"Towards Improving the Anti-attack Capability of the RangeNet++" by Qingguo Zhou, Ming Lei et al.

With the possibility of deceiving deep learning models by appropriately modifying images verified, lots of researches on adversarial attacks and adversarial defenses have been carried out in academia. However, there is few research on adversarial attacks and adversarial defenses of point cloud semantic segmentation models, especially in the field of autonomous driving. The stability and robustness of point cloud semantic segmentation models are our primary concerns in this paper. Aiming at the point cloud segmentation model RangeNet++ in the field of autonomous driving, we propose novel approaches to improve the security and anti-attack capability of the RangeNet++ model. One is to calculate the local geometry that can reflect the surface shape of the point cloud based on the range image. The other is to obtain a general adversarial sample related only to the image itself and closer to the real world based on the range image, then add it into the training set for training. The experime ....

Adversarial Attacks , Adversarial Defenses , Adversarial Samples , Ocal Geometry , Angenet , Semantic Segmentation ,

Using AI to Reduce IoT Vulnerability

This article considers the use of artificial intelligence to help security professionals protect IoT systems. ....

Ben Pilkingtonjan , Ben Pilkington , Tippapatt Shutterstock , Sn Applied Sciences , Fourth Industrial Revolution , Annual Conference , Artificial Intelligence , Malicious Attacks , Nonlinear Cyber Physical Systems , Discover Internet , Robust Pervasive Detection , Adversarial Samples , Nternet Of Things ,