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
01.02.2020
170964 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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