"Use It-No Need to Shake It!" Accurate Implicit Authentication for Everyday Objects with Smart Sensing

Chuxiong Wu, Xiaopeng Li, Fei Zuo, Lannan Luo, Xiaojiang Du, Jia Di, Qiang Zeng

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Implicit authentication for traditional objects, such as doors and dumbbells, has rich applications but is rarely studied. An ongoing trend is that traditional objects are retrofitted to smart environments; for instance, a contact sensor is attached to a door to detect door opening (but cannot tell "who is opening the door"). We present the first accurate implicit-authentication system for retrofitted everyday objects, named MoMatch. It makes an authentication decision based on a single natural object use, unlike prior work that requires shaking objects. MoMatch is built on the observation that an object has a motion typically because a human hand moves it; thus, the object's motion and the legitimate user's hand movement should correlate. The main challenge is, given the small amount of data collected during one object use, how to measure the correlation accurately. We convert the correlation measurement problem into an image comparison problem and resolve it using neural networks successfully. MoMatch does not need to profile the user's biometric information and is resilient to mimicry attacks.

Original languageEnglish
Article number146
JournalProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Volume6
Issue number3
DOIs
StatePublished - 7 Sep 2022

Keywords

  • Deep Learning
  • Everyday Objects
  • Implicit Authentication
  • Smart Sensing

Fingerprint

Dive into the research topics of '"Use It-No Need to Shake It!" Accurate Implicit Authentication for Everyday Objects with Smart Sensing'. Together they form a unique fingerprint.

Cite this