Capacitive Touchscreens at Risk: Recovering Handwritten Trajectory on Smartphone via Electromagnetic Emanations

TL;DR

This paper introduces TESLA, a non-contact electromagnetic side-channel attack that can recover detailed handwritten trajectories on smartphones with high accuracy, exposing a significant security vulnerability.

Contribution

The paper demonstrates a novel electromagnetic side-channel attack framework capable of real-time handwriting trajectory recovery on commercial smartphones.

Findings

Achieves 77% character recognition accuracy

Jaccard index of 0.74 for trajectory similarity

Effectively recovers recognizable handwriting trajectories

Abstract

This paper reveals and exploits a critical security vulnerability: the electromagnetic (EM) side channel of capacitive touchscreens leaks sufficient information to recover fine-grained, continuous handwriting trajectories. We present Touchscreen Electromagnetic Side-channel Leakage Attack (TESLA), a non-contact attack framework that captures EM signals generated during on-screen writing and regresses them into two-dimensional (2D) handwriting trajectories in real time. Extensive evaluations across a variety of commercial off-the-shelf (COTS) smartphones show that TESLA achieves 77% character recognition accuracy and a Jaccard index of 0.74, demonstrating its capability to recover highly recognizable motion trajectories that closely resemble the original handwriting under realistic attack conditions.