Boosted by the evolution of machine learning technology, large amount of data and advanced computing system, neural networks have achieved state-of-the-art performance that even exceeds human capability in many applications [1] [2] . However, adversarial attacks targeting neural networks have demonstrated detrimental impact in autonomous driving [3] . The adversarial attacks are capable of arbitrarily manipulating the neural network classification results with different input data which is non-perceivable to human.
An Autoencoder Based Approach to Defend Against Adversarial Attacks for Autonomous Vehicles
2020-02-01
170964 byte
Conference paper
Electronic Resource
English
ICAS2016_0221: SECURE ESTIMATION FOR UNMANNED AERIAL VEHICLES AGAINST ADVERSARIAL ATTACKS
British Library Conference Proceedings | 2016
|ROBUST TRAJECTORY PREDICTIONS AGAINST ADVERSARIAL ATTACKS IN AUTONOMOUS MACHINES AND APPLICATIONS
European Patent Office | 2024
|Securing deep learning against adversarial attacks for connected and automated vehicles
TIBKAT | 2022
|