Driving into Danger: Adversarial Patch Attack on End-to-End Autonomous Driving Systems Using Deep Learning
Author:
Affiliation:
1. Institute of System Engineering, Academy of Military Sciences,Beijing,China
2. Tsinghua University,Beijing,China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10217219/10217829/10218291.pdf?arnumber=10218291
Reference22 articles.
1. Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses
2. A Survey on Automated Driving System Testing: Landscapes and Trends
3. Perceptual-Sensitive GAN for Generating Adversarial Patches
4. Extended Spatially Localized Perturbation GAN (eSLP-GAN) for Robust Adversarial Camouflage Patches
5. Fooling Automated Surveillance Cameras: Adversarial Patches to Attack Person Detection
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