Phantom of the ADAS: Securing Advanced Driver-Assistance Systems from Split-Second Phantom Attacks

Ben Nassi, Yisroel Mirsky, Dudi Nassi, Raz Ben-Netanel, Oleg Drokin, Yuval Elovici

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, we investigate "split-second phantom attacks,"a scientific gap that causes two commercial advanced driver-assistance systems (ADASs), Telsa Model X (HW 2.5 and HW 3) and Mobileye 630, to treat a depthless object that appears for a few milliseconds as a real obstacle/object. We discuss the challenge that split-second phantom attacks create for ADASs. We demonstrate how attackers can apply split-second phantom attacks remotely by embedding phantom road signs into an advertisement presented on a digital billboard which causes Tesla's autopilot to suddenly stop the car in the middle of a road and Mobileye 630 to issue false notifications. We also demonstrate how attackers can use a projector in order to cause Tesla's autopilot to apply the brakes in response to a phantom of a pedestrian that was projected on the road and Mobileye 630 to issue false notifications in response to a projected road sign. To counter this threat, we propose a countermeasure which can determine whether a detected object is a phantom or real using just the camera sensor. The countermeasure (GhostBusters) uses a "committee of experts"approach and combines the results obtained from four lightweight deep convolutional neural networks that assess the authenticity of an object based on the object's light, context, surface, and depth. We demonstrate our countermeasure's effectiveness (it obtains a TPR of 0.994 with an FPR of zero) and test its robustness to adversarial machine learning attacks.

Original languageAmerican English
Title of host publicationCCS 2020 - Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security
Pages293-308
Number of pages16
ISBN (Electronic)9781450370899
DOIs
StatePublished - 30 Oct 2020
Event27th ACM SIGSAC Conference on Computer and Communications Security, CCS 2020 - Virtual, Online, United States
Duration: 9 Nov 202013 Nov 2020

Publication series

NameProceedings of the ACM Conference on Computer and Communications Security

Conference

Conference27th ACM SIGSAC Conference on Computer and Communications Security, CCS 2020
Country/TerritoryUnited States
CityVirtual, Online
Period9/11/2013/11/20

Keywords

  • advanced driver-assistance systems
  • neural-networks
  • security
  • split-second phantom attacks

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications

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