TapLogger: inferring user inputs on smartphone touchscreens using on-board motion sensors

By Zhi Xu, Kun Bai, Sencun Zhu. Wireless Network Security, 2012

386 citations recorded by the source corpus.

Subfields: Computer Science Applications, Computer Vision and Pattern Recognition, Transportation

View Paper: https://www.semanticscholar.org/paper/7f6fdcd579bc17734f25c88d69ab476e31bc95af

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Science Applications

1. Kang G. Shin
2. Kassem Fawaz
3. Huan Feng
4. Prateek Mittal
5. Supriyo Chakraborty
6. Changchang Liu
7. Romit Roy Choudhury
8. Peter Kairouz

In Computer Vision and Pattern Recognition

1. Kang G. Shin
2. Kassem Fawaz
3. Huan Feng
4. Prateek Mittal
5. Supriyo Chakraborty
6. Patrick Traynor
7. Changchang Liu
8. Romit Roy Choudhury

In Transportation

1. Kang G. Shin
2. Kassem Fawaz
3. Huan Feng
4. Prateek Mittal
5. Supriyo Chakraborty
6. Changchang Liu
7. Romit Roy Choudhury
8. Wenyuan Xu

Citations in the corpus

• : Privacy on Mobile Devices – It ’ s Complicated (2016, methodology): “Similarly, data from the device’s accelerometer and/or microphone has been leveraged to infer user key presses [4] and tap locations [3, 123] on the touch screen.”
• ARMageddon: Cache Attacks on Mobile Devices (2015, background): “, the accelerometer and the gyroscope [8,12,13,40,60] or the ambient-light sensor [50].”
• Continuous Authentication for Voice Assistants (2017, methodology): “Our implementation is integrated into the Google Now system in Android, and could easily extend to other voice-based platforms such as Cortana, Siri, or even phone banking services.”
• You Are How You Touch: User Verification on Smartphones via Tapping Behaviors (2014, background): “However, given the same mobile device, behavioral biometrics tend to be less sensitive to the surrounding environmental factors like darkness or noise.”
• *droid (2016, background): “We refer readers to that work for more information on Android test generation [Amalfitano et al. 2012; Aviv et al. 2012; Xu et al. 2012; Gomez et al. 2013; Machiry et al. 2013; Hao et al. 2014; Liang et al. 2014; Mahmood et al. 2014; Narain et al. 2014].”
• AccelPrint: Imperfections of Accelerometers Make Smartphones Trackable (2014, background): “, TouchLogger [15], ACCessory [49], Taplogger [67]) have been built.”
• SoK: Privacy on Mobile Devices – It’s Complicated (2016, methodology): “Similarly, data from the device’s accelerometer and/or microphone has been leveraged to infer user key presses [4] and tap locations [3, 123] on the touch screen.”
• DEEProtect: Enabling Inference-based Access Control on Mobile Sensing Applications (2017, background): “, Passwords or PINs) (sensitive inference) [91], [62], [13], [59].”