Capacitive Touchscreens at Risk: A Practical Side-Channel Attack on Smartphones via Electromagnetic Emanations

2026-05-14

AI Summary

This work addresses the electromagnetic side-channel leakage inherent in modern capacitive touchscreens, which has been largely overlooked due to the invasive or highly constrained nature of existing attacks. The authors propose TESLA, a non-invasive electromagnetic side-channel attack that leverages the intrinsic electromagnetic emissions generated during touchscreen scanning. By deploying near-field electromagnetic probes and integrating signal processing, spatiotemporal feature extraction, and machine learning, TESLA effectively reconstructs user inputs—including PINs, keyboard keystrokes, application usage, and handwritten trajectories. Evaluated on iPhone X, Xiaomi 10 Pro, Samsung S10, and Huawei Mate 30 Pro, the method achieves 99.3% accuracy in PIN recovery, 97.6% in keyboard reconstruction, 95.0% in application inference, 76.8% in handwritten character recognition, and a Jaccard similarity of 0.74 for trajectory reconstruction, substantially enhancing the practicality and scope of such side-channel attacks.

Abstract

Capacitive touchscreens in modern smartphones introduce severe side-channel vulnerabilities. However, existing attacks often require restrictive conditions or invasive measurements. This paper presents TESLA, a novel, contactless electromagnetic (EM) side-channel attack that exploits inherent EM emanations during touchscreen scanning. We demonstrate that these emanations encode the spatiotemporal evolution of touch interactions, forming a unified leakage basis. By secretly placing an EM probe near the victim's device, TESLA enables attackers to extract highly sensitive information, including screen-unlocking PIN codes, keyboard inputs, interacting application categories, and continuous handwriting trajectories. Compared to existing attacks, TESLA offers a broader range of attack targets, more efficient sample acquisition, and operations in practical attack scenarios. Extensive evaluations on popular commercial smartphones, specifically the iPhone X, Xiaomi 10 Pro, Samsung S10, and Huawei Mate 30 Pro, validate the effectiveness of TESLA. It achieves remarkable inference accuracy in diverse settings such as private meeting rooms and public libraries, with success rates of 99.3% for PIN code recognition, 97.6% for keyboard input reconstruction, and 95.0% for application inference, respectively. Simultaneously, it attains a 76.8% character recognition accuracy and a high geometric similarity (Jaccard index of 0.74) for 2D handwriting trajectory reconstruction.

Problem

• capacitive touchscreen
• side-channel attack
• electromagnetic emanations
• smartphone security
• information leakage

Innovation

• electromagnetic side-channel attack
• capacitive touchscreen
• contactless eavesdropping
• touch interaction leakage
• TESLA