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

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 retro.tted 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.rst accurate implicit-authentication system for retro.tted 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 pro.le the user's biometric information and is resilient to mimicry attacks.