This Car is Mine!: Automobile Theft Countermeasure Leveraging Driver Identification with Generative Adversarial Networks
This addresses automobile theft for car owners and manufacturers by providing a practical countermeasure, though it is incremental as it builds on existing data mining and biometric methods.
The paper tackled the problem of automobile theft by proposing a driver identification method using Generative Adversarial Networks (GANs) that learns only from the owner driver's data, eliminating the need for thief data, and demonstrated effective recognition of the owner driver from actual driving data.
As a car becomes more connected, a countermeasure against automobile theft has become a significant task in the real world. To respond to automobile theft, data mining, biometrics, and additional authentication methods are proposed. Among current countermeasures, data mining method is one of the efficient ways to capture the owner driver's unique characteristics. To identify the owner driver from thieves, previous works applied various algorithms toward driving data. Such data mining methods utilized supervised learning, thus required labeled data set. However, it is unrealistic to gather and apply the thief's driving pattern. To overcome this problem, we propose driver identification method with GAN. GAN has merit to build identification model by learning the owner driver's data only. We trained GAN only with owner driver's data and used trained discriminator to identify the owner driver. From actual driving data, we evaluated our identification model recognizes the owner driver well. By ensembling various driver authentication methods with the proposed model, we expect industry can develop automobile theft countermeasures available in the real world.