Autonomous vehicles (AVs) use diverse sensors to understand their surroundings as they continually make safety-critical decisions. However, establishing trust with other AVs is a key prerequisite because safety-critical decisions cannot be made based on data shared from untrusted sources. Existing protocols require an infrastructure network connection and a third-party root of trust to establish a secure channel, which are not always available.In this paper, we propose a sensor-fusion approach for mobile trust establishment, which combines GPS and visual data. The combined data forms evidence that one vehicle is nearby another, which is a strong indication that it is not a remote adversary hence trustworthy. Our preliminary experiments show that our sensor-fusion approach achieves above 80% successful pairing of two legitimate vehicles observing the same object with 5 meters of error. Based on these preliminary results, we anticipate that a refined approach can support fuzzy trust establishment, enabling better collaboration between nearby AVs.
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Multibiometric secure system based on deep learning
In this paper, we propose a secure multibiometric system that
uses deep neural networks and error-correction coding. We
present a feature-level fusion framework to generate a secure
multibiometric template from each user’s multiple biometrics.
Two fusion architectures, fully connected architecture
and bilinear architecture, are implemented to develop a robust
multibiometric shared representation. The shared representation
is used to generate a cancelable biometric template
that involves the selection of a different set of reliable and
discriminative features for each user. This cancelable template
is a binary vector and is passed through an appropriate
error-correcting decoder to find a closest codeword and
this codeword is hashed to generate the final secure template.
The efficacy of the proposed approach is shown using a multimodal
database where we achieve state-of-the-art matching
performance, along with cancelability and security.
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- NSF-PAR ID:
- 10053523
- Date Published:
- Journal Name:
- Proc. IEEE Global Conf. on Signal and Information Processing (GlobalSIP)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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