hal-00761669, version 1

A Fast Eavesdropping Attack Against Touchscreens

Federico Maggi ^a, 1, Alberto Volpatto () ¹, Simone Gasparini (, ) ^b, 2, Giacomo Boracchi ¹, Stefano Zanero () ¹

7th International Conference on Information Assurance and Security (IAS), 2011 (2011)

• a –  Politecnico di Milano
• b –  INRIA
• 1:  Dipartimento di Elettronica e Informazione
• http://www.elet.polimi.it/index.jsp
  Politecnico di Milano Piazza Leonardo da Vinci 32, 20133 Milano Italy
• 2:  PERCEPTION (INRIA Grenoble Rhône-Alpes / LJK Laboratoire Jean Kuntzmann)
• http://perception.inrialpes.fr/
  INRIA – Laboratoire Jean Kuntzmann – CNRS : UMR5224 – Université Joseph Fourier - Grenoble I – Institut National Polytechnique de Grenoble (INPG) – Université Pierre-Mendès-France - Grenoble II INRIA Rhône-Alpes 655, avenue de l'Europe 38330 Montbonnot, France France

Bibliographic reference

• Type of document: Peer-reviewed conferences/proceedings
• Domain:

  Computer Science/Computer Vision and Pattern Recognition

  Computer Science/Cryptography and Security

• Title: A Fast Eavesdropping Attack Against Touchscreens
• Abstract: The pervasiveness of mobile devices increases the risk of exposing sensitive information on the go. In this paper, we arise this concern by presenting an automatic attack against mod- ern touchscreen keyboards. We demonstrate the attack against the Apple iPhone--2010's most popular touchscreen device-- although it can be adapted to other devices (e.g., Android) that employ similar key-magnifying keyboards. Our attack processes the stream of frames from a video camera (e.g., surveillance or portable camera) and recognizes keystrokes online, in a fraction of the time needed to perform the same task by direct observation or offline analysis of a recorded video, which can be unfeasible for large amount of data. Our attack detects, tracks, and rectifies the target touchscreen, thus following the device or camera's movements and eliminating possible perspective distortions and rotations In real-world settings, our attack can automatically recognize up to 97.07 percent of the keystrokes (91.03 on average), with 1.15 percent of errors (3.16 on average) at a speed ranging from 37 to 51 keystrokes per minute.
• Full text language: English
• Publication date: 2011-12-05
• Audience: international
• Conference title: 7th International Conference on Information Assurance and Security (IAS), 2011
• Conference city: Melaka
• Country: Malaysia
• Conference date: 2011-12-05
• Conference date (end): 2012-12-08
• DOI: 10.1109/ISIAS.2011.6122840

Attached file list to this document: 

• hal-00761669, version 1
• http://hal.inria.fr/hal-00761669
• oai:hal.inria.fr:hal-00761669
• From: Simone Gasparini <>
• Submitted on: Wednesday, 5 December 2012 21:42:01
• Updated on: Tuesday, 11 December 2012 13:57:14